first commit

.gitignore

@@ -0,0 +1,2 @@
+.env
+env

app.py

@@ -0,0 +1,86 @@
+from transformers import pipeline
+import torch
+
+import os
+import openai
+from dotenv import load_dotenv
+from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
+from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
+
+model_id = "openai/whisper-base" 
+pipe = pipeline("automatic-speech-recognition", model=model_id)
+
+def transcribe_speech(filepath):
+    output = pipe(
+        filepath,
+        max_new_tokens=256,
+        generate_kwargs={
+            "task": "transcribe",
+            "language": "spanish",
+        },  # update with the language you've fine-tuned on
+        chunk_length_s=30,
+        batch_size=8,
+    )
+    return output["text"]
+
+
+# Load environment variables from the .env file de forma local
+load_dotenv()
+openai.api_key = os.environ['OPENAI_API_KEY']
+
+def query_chatgpt(text):
+    messages = []
+    messages.append({'role': 'user', 'content': '{}'.format(text)})
+    print("Preguntando "+text)
+    response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages= messages,
+            temperature=0.5,
+            max_tokens=256
+            ).choices[0].message.content
+    return response
+
+
+
+
+models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
+    "facebook/tts_transformer-es-css10",
+    arg_overrides={"vocoder": "hifigan", "fp16": False}
+)
+model = models[0]
+TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
+generator = task.build_generator([model], cfg)
+
+# text = "Había una vez."
+
+# sample = TTSHubInterface.get_model_input(task, text)
+# wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
+
+# ipd.Audio(wav, rate=rate)
+from tts import synthesize
+
+
+def syn_facebookmms(text):
+    sample = TTSHubInterface.get_model_input(task, text)
+    wav,rate = TTSHubInterface.get_prediction(task, model, generator, sample)
+    return wav,rate
+
+def answer_question(filepath):
+    transcription = transcribe_speech(filepath)
+    response = query_chatgpt(transcription)
+    # audio = synthesise(response)
+    print(response)
+    # audio, rate = syn_facebookmms(response)
+    rate,audio = synthesize(response,1,"spa")
+    print(audio)
+    return rate,audio
+
+
+import gradio as gr
+with gr.Blocks() as demo:
+    entrada = gr.Audio(source="microphone",type="filepath")
+    boton = gr.Button("Responder")
+
+    salida = gr.Audio()
+    boton.click(answer_question,entrada,salida)
+demo.launch(debug=True)

app2.py

@@ -0,0 +1,128 @@
+from transformers import pipeline
+import torch
+from IPython.display import Audio
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+classifier = pipeline(
+    "audio-classification", model="MIT/ast-finetuned-speech-commands-v2", device=device
+)
+
+from transformers.pipelines.audio_utils import ffmpeg_microphone_live
+
+print(classifier.model.config.id2label)
+
+def launch_fn(
+    wake_word="marvin",
+    prob_threshold=0.5,
+    chunk_length_s=2.0,
+    stream_chunk_s=0.25,
+    debug=False,
+):
+    if wake_word not in classifier.model.config.label2id.keys():
+        raise ValueError(
+            f"Wake word {wake_word} not in set of valid class labels, pick a wake word in the set {classifier.model.config.label2id.keys()}."
+        )
+
+    sampling_rate = classifier.feature_extractor.sampling_rate
+
+    mic = ffmpeg_microphone_live(
+        sampling_rate=sampling_rate,
+        chunk_length_s=chunk_length_s,
+        stream_chunk_s=stream_chunk_s,
+    )
+
+    print("Listening for wake word...")
+    for prediction in classifier(mic):
+        prediction = prediction[0]
+        if debug:
+            print(prediction)
+        if prediction["label"] == wake_word:
+            if prediction["score"] > prob_threshold:
+                return True
+            
+# launch_fn(debug=True)
+
+transcriber = pipeline(
+    "automatic-speech-recognition", model="openai/whisper-base.en", device=device
+)
+
+import sys
+
+
+def transcribe(chunk_length_s=5.0, stream_chunk_s=1.0):
+    sampling_rate = transcriber.feature_extractor.sampling_rate
+
+    mic = ffmpeg_microphone_live(
+        sampling_rate=sampling_rate,
+        chunk_length_s=chunk_length_s,
+        stream_chunk_s=stream_chunk_s,
+    )
+
+    print("Start speaking...")
+    for item in transcriber(mic, generate_kwargs={"max_new_tokens": 128}):
+        sys.stdout.write("\033[K")
+        print(item["text"], end="\r")
+        if not item["partial"][0]:
+            break
+
+    return item["text"]
+
+from huggingface_hub import HfFolder
+import requests
+
+
+def query(text, model_id="tiiuae/falcon-7b-instruct"):
+    api_url = f"https://api-inference.huggingface.co/models/{model_id}"
+    headers = {"Authorization": f"Bearer {HfFolder().get_token()}"}
+    payload = {"inputs": text}
+
+    print(f"Querying...: {text}")
+    response = requests.post(api_url, headers=headers, json=payload)
+    return response.json()[0]["generated_text"][len(text) + 1 :]
+
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
+
+from datasets import load_dataset
+
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+
+def synthesise(text):
+    inputs = processor(text=text, return_tensors="pt")
+    speech = model.generate_speech(
+        inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder
+    )
+    return speech.cpu()
+
+
+# launch_fn()
+# print("hablá")
+# transcription = transcribe()
+# response = query(transcription)
+# audio = synthesise(response)
+
+
+audio = synthesise(
+    "Hugging Face is a company that provides natural language processing and machine learning tools for developers."
+)
+
+
+
+
+
+# import gradio as gr
+
+# with gr.Blocks() as demo:
+#     boton = gr.Button("hablar")
+#     audio = gr.Audio()
+#     micro = gr.Microphone()
+#     boton.click(start,micro,audio)
+
+

data/tts/all_langs.tsv

@@ -0,0 +1,1143 @@
+abi Abidji
+ace Aceh
+aca Achagua
+acn Achang
+acr Achi
+ach Acholi
+acu Achuar-Shiwiar
+guq Aché
+ade Adele
+adj Adioukrou
+agd Agarabi
+agx Aghul
+agn Agutaynen
+aha Ahanta
+aka Akan
+knj Akateko
+ake Akawaio
+aeu Akeu
+ahk Akha
+bss Akoose
+alj Alangan
+sqi Albanian
+alt Altai, Southern
+alp Alune
+alz Alur
+kab Amazigh
+amk Ambai
+mmg Ambrym, North
+amh Amharic
+ami Amis
+azg Amuzgo, San Pedro Amuzgos
+agg Angor
+boj Anjam
+cko Anufo
+any Anyin
+arl Arabela
+ara Arabic
+atq Aralle-Tabulahan
+luc Aringa
+hyw Armenian, Western
+apr Arop-Lokep
+aia Arosi
+msy Aruamu
+cni Asháninka
+cjo Ashéninka, Pajonal
+cpu Ashéninka, Pichis
+cpb Ashéninka, Ucayali-Yurúa
+asm Assamese
+asa Asu
+teo Ateso
+ati Attié
+djk Aukan
+ava Avar
+avn Avatime
+avu Avokaya
+awb Awa
+kwi Awa-Cuaiquer
+awa Awadhi
+agr Awajún
+agu Awakateko
+ayr Aymara, Central
+ayo Ayoreo
+abp Ayta, Abellen
+blx Ayta, Mag-Indi
+sgb Ayta, Mag-antsi
+azj-script_cyrillic Azerbaijani, North
+azj-script_latin Azerbaijani, North
+azb Azerbaijani, South
+bba Baatonum
+bhz Bada
+bvc Baelelea
+bfy Bagheli
+bgq Bagri
+bdq Bahnar
+bdh Baka
+bqi Bakhtiâri
+bjw Bakwé
+blz Balantak
+ban Bali
+bcc-script_latin Balochi, Southern
+bcc-script_arabic Balochi, Southern
+bam Bamanankan
+ptu Bambam
+bcw Bana
+bqj Bandial
+bno Bantoanon
+bbb Barai
+bfa Bari
+bjz Baruga
+bak Bashkort
+eus Basque
+bsq Bassa
+akb Batak Angkola
+btd Batak Dairi
+btx Batak Karo
+bts Batak Simalungun
+bbc Batak Toba
+bvz Bauzi
+bjv Bedjond
+bep Behoa
+bkv Bekwarra
+bzj Belize English Creole
+bem Bemba
+bng Benga
+ben Bengali
+bom Berom
+btt Bete-Bendi
+bha Bharia
+bgw Bhatri
+bht Bhattiyali
+beh Biali
+sne Bidayuh, Bau
+ubl Bikol, Buhi’non
+bcl Bikol, Central
+bim Bimoba
+bkd Binukid
+bjr Binumarien
+bfo Birifor, Malba
+biv Birifor, Southern
+bib Bisa
+bis Bislama
+bzi Bisu
+bqp Bisã
+bpr Blaan, Koronadal
+bps Blaan, Sarangani
+bwq Bobo Madaré, Southern
+bdv Bodo Parja
+bqc Boko
+bus Bokobaru
+bnp Bola
+bmq Bomu
+bdg Bonggi
+boa Bora
+ksr Borong
+bor Borôro
+bru Bru, Eastern
+box Buamu
+bzh Buang, Mapos
+bgt Bughotu
+sab Buglere
+bul Bulgarian
+bwu Buli
+bmv Bum
+mya Burmese
+tte Bwanabwana
+cjp Cabécar
+cbv Cacua
+kaq Capanahua
+cot Caquinte
+cbc Carapana
+car Carib
+cat Catalan
+ceb Cebuano
+cme Cerma
+cbi Chachi
+ceg Chamacoco
+cly Chatino, Eastern Highland
+cya Chatino, Nopala
+che Chechen
+hne Chhattisgarhi
+nya Chichewa
+dig Chidigo
+dug Chiduruma
+bgr Chin, Bawm
+cek Chin, Eastern Khumi
+cfm Chin, Falam
+cnh Chin, Hakha
+hlt Chin, Matu
+mwq Chin, Müün
+ctd Chin, Tedim
+tcz Chin, Thado
+zyp Chin, Zyphe
+cco Chinantec, Comaltepec
+cnl Chinantec, Lalana
+cle Chinantec, Lealao
+chz Chinantec, Ozumacín
+cpa Chinantec, Palantla
+cso Chinantec, Sochiapam
+cnt Chinantec, Tepetotutla
+cuc Chinantec, Usila
+hak Chinese, Hakka
+nan Chinese, Min Nan
+xnj Chingoni
+cap Chipaya
+cax Chiquitano
+ctg Chittagonian
+ctu Chol
+chf Chontal, Tabasco
+cce Chopi
+crt Chorote, Iyojwa’ja
+crq Chorote, Iyo’wujwa
+cac-dialect_sansebastiáncoatán Chuj
+cac-dialect_sanmateoixtatán Chuj
+ckt Chukchi
+ncu Chumburung
+cdj Churahi
+chv Chuvash
+caa Ch’orti’
+asg Cishingini
+con Cofán
+crn Cora, El Nayar
+cok Cora, Santa Teresa
+crk-script_latin Cree, Plains
+crk-script_syllabics Cree, Plains
+crh Crimean Tatar
+cui Cuiba
+dsh Daasanach
+dbq Daba
+dga Dagaare, Southern
+dgi Dagara, Northern
+dgk Dagba
+dnj-dialect_gweetaawueast Dan
+dnj-dialect_blowowest Dan
+daa Dangaléat
+dnt Dani, Mid Grand Valley
+dnw Dani, Western
+dar Dargwa
+tcc Datooga
+dwr Dawro
+ded Dedua
+mzw Deg
+ntr Delo
+ddn Dendi
+des Desano
+dso Desiya
+nfa Dhao
+dhi Dhimal
+gud Dida, Yocoboué
+did Didinga
+mhu Digaro-Mishmi
+dip Dinka, Northeastern
+dik Dinka, Southwestern
+tbz Ditammari
+dts Dogon, Toro So
+dos Dogosé
+dgo Dogri
+mvp Duri
+nld Dutch
+jen Dza
+dzo Dzongkha
+idd Ede Idaca
+eka Ekajuk
+cto Embera Catío
+emp Emberá, Northern
+eng English
+enx Enxet
+sja Epena
+myv Erzya
+mcq Ese
+ese Ese Ejja
+evn Evenki
+eza Ezaa
+fal Fali, South
+fao Faroese
+far Fataleka
+fij Fijian
+fin Finnish
+fon Fon
+frd Fordata
+fra French
+ful Fulah
+flr Fuliiru
+gau Gadaba, Mudhili
+gbk Gaddi
+gag-script_cyrillic Gagauz
+gag-script_latin Gagauz
+gbi Galela
+gmv Gamo
+lug Ganda
+pwg Gapapaiwa
+gbm Garhwali
+cab Garifuna
+grt Garo
+krs Gbaya
+gso Gbaya, Southwest
+nlg Gela
+gej Gen
+deu German, Standard
+gri Ghari
+kik Gikuyu
+acd Gikyode
+glk Gilaki
+gof-script_latin Gofa
+gog Gogo
+gkn Gokana
+wsg Gondi, Adilabad
+gjn Gonja
+gqr Gor
+gor Gorontalo
+gux Gourmanchéma
+gbo Grebo, Northern
+ell Greek
+grc Greek, Ancient
+guh Guahibo
+gub Guajajára
+grn Guarani
+gyr Guarayu
+guo Guayabero
+gde Gude
+guj Gujarati
+gvl Gulay
+guk Gumuz
+rub Gungu
+dah Gwahatike
+gwr Gwere
+gwi Gwich’in
+hat Haitian Creole
+hlb Halbi
+amf Hamer-Banna
+hag Hanga
+hnn Hanunoo
+bgc Haryanvi
+had Hatam
+hau Hausa
+hwc Hawaii Pidgin
+hvn Hawu
+hay Haya
+xed Hdi
+heb Hebrew
+heh Hehe
+hil Hiligaynon
+hin Hindi
+hif Hindi, Fiji
+hns Hindustani, Sarnami
+hoc Ho
+hoy Holiya
+hus-dialect_westernpotosino Huastec
+hus-dialect_centralveracruz Huastec
+huv Huave, San Mateo del Mar
+hui Huli
+hun Hungarian
+hap Hupla
+iba Iban
+isl Icelandic
+dbj Ida’an
+ifa Ifugao, Amganad
+ifb Ifugao, Batad
+ifu Ifugao, Mayoyao
+ifk Ifugao, Tuwali
+ife Ifè
+ign Ignaciano
+ikk Ika
+iqw Ikwo
+ilb Ila
+ilo Ilocano
+imo Imbongu
+ind Indonesian
+inb Inga
+ipi Ipili
+irk Iraqw
+icr Islander English Creole
+itv Itawit
+itl Itelmen
+atg Ivbie North-Okpela-Arhe
+ixl-dialect_sanjuancotzal Ixil
+ixl-dialect_sangasparchajul Ixil
+ixl-dialect_santamarianebaj Ixil
+nca Iyo
+izr Izere
+izz Izii
+jac Jakalteko
+jam Jamaican English Creole
+jav Javanese
+jvn Javanese, Suriname
+kac Jingpho
+dyo Jola-Fonyi
+csk Jola-Kasa
+adh Jopadhola
+jun Juang
+jbu Jukun Takum
+dyu Jula
+bex Jur Modo
+juy Juray
+gna Kaansa
+urb Kaapor
+kbp Kabiyè
+cwa Kabwa
+dtp Kadazan Dusun
+kbr Kafa
+cgc Kagayanen
+kki Kagulu
+kzf Kaili, Da’a
+lew Kaili, Ledo
+cbr Kakataibo-Kashibo
+kkj Kako
+keo Kakwa
+kqe Kalagan
+kak Kalanguya
+kyb Kalinga, Butbut
+knb Kalinga, Lubuagan
+kmd Kalinga, Majukayang
+kml Kalinga, Tanudan
+ify Kallahan, Keley-i
+xal Kalmyk-Oirat
+kbq Kamano
+kay Kamayurá
+ktb Kambaata
+hig Kamwe
+gam Kandawo
+cbu Kandozi-Chapra
+xnr Kangri
+kmu Kanite
+kne Kankanaey
+kan Kannada
+kby Kanuri, Manga
+pam Kapampangan
+cak-dialect_santamaríadejesús Kaqchikel
+cak-dialect_southcentral Kaqchikel
+cak-dialect_yepocapa Kaqchikel
+cak-dialect_western Kaqchikel
+cak-dialect_santodomingoxenacoj Kaqchikel
+cak-dialect_central Kaqchikel
+xrb Karaboro, Eastern
+krc Karachay-Balkar
+kaa Karakalpak
+krl Karelian
+pww Karen, Pwo Northern
+xsm Kasem
+cbs Kashinawa
+pss Kaulong
+kxf Kawyaw
+kyz Kayabí
+kyu Kayah, Western
+txu Kayapó
+kaz Kazakh
+ndp Kebu
+kbo Keliko
+kyq Kenga
+ken Kenyang
+ker Kera
+xte Ketengban
+kyg Keyagana
+kjh Khakas
+kca Khanty
+khm Khmer
+kxm Khmer, Northern
+kjg Khmu
+nyf Kigiryama
+kij Kilivila
+kia Kim
+kqr Kimaragang
+kqp Kimré
+krj Kinaray-a
+zga Kinga
+kin Kinyarwanda
+pkb Kipfokomo
+geb Kire
+gil Kiribati
+kje Kisar
+kss Kisi, Southern
+thk Kitharaka
+klu Klao
+kyo Klon
+kog Kogi
+kfb Kolami, Northwestern
+kpv Komi-Zyrian
+bbo Konabéré
+xon Konkomba
+kma Konni
+kno Kono
+kxc Konso
+ozm Koonzime
+kqy Koorete
+kor Korean
+coe Koreguaje
+kpq Korupun-Sela
+kpy Koryak
+kyf Kouya
+kff-script_telugu Koya
+kri Krio
+rop Kriol
+ktj Krumen, Plapo
+ted Krumen, Tepo
+krr Krung
+kdt Kuay
+kez Kukele
+cul Kulina
+kle Kulung
+kdi Kumam
+kue Kuman
+kum Kumyk
+kvn Kuna, Border
+cuk Kuna, San Blas
+kdn Kunda
+xuo Kuo
+key Kupia
+kpz Kupsapiiny
+knk Kuranko
+kmr-script_latin Kurdish, Northern
+kmr-script_arabic Kurdish, Northern
+kmr-script_cyrillic Kurdish, Northern
+xua Kurumba, Alu
+kru Kurux
+kus Kusaal
+kub Kutep
+kdc Kutu
+kxv Kuvi
+blh Kuwaa
+cwt Kuwaataay
+kwd Kwaio
+tnk Kwamera
+kwf Kwara’ae
+cwe Kwere
+kyc Kyaka
+tye Kyanga
+kir Kyrgyz
+quc-dialect_north K’iche’
+quc-dialect_east K’iche’
+quc-dialect_central K’iche’
+lac Lacandon
+lsi Lacid
+lbj Ladakhi
+lhu Lahu
+las Lama
+lam Lamba
+lns Lamnso’
+ljp Lampung Api
+laj Lango
+lao Lao
+lat Latin
+lav Latvian
+law Lauje
+lcp Lawa, Western
+lzz Laz
+lln Lele
+lef Lelemi
+acf Lesser Antillean French Creole
+lww Lewo
+mhx Lhao Vo
+eip Lik
+lia Limba, West-Central
+lif Limbu
+onb Lingao
+lis Lisu
+loq Lobala
+lob Lobi
+yaz Lokaa
+lok Loko
+llg Lole
+ycl Lolopo
+lom Loma
+ngl Lomwe
+lon Lomwe, Malawi
+lex Luang
+lgg Lugbara
+ruf Luguru
+dop Lukpa
+lnd Lundayeh
+ndy Lutos
+lwo Luwo
+lee Lyélé
+mev Maan
+mfz Mabaan
+jmc Machame
+myy Macuna
+mbc Macushi
+mda Mada
+mad Madura
+mag Magahi
+ayz Mai Brat
+mai Maithili
+mca Maka
+mcp Makaa
+mak Makasar
+vmw Makhuwa
+mgh Makhuwa-Meetto
+kde Makonde
+mlg Malagasy
+zlm Malay
+pse Malay, Central
+mkn Malay, Kupang
+xmm Malay, Manado
+mal Malayalam
+xdy Malayic Dayak
+div Maldivian
+mdy Male
+mup Malvi
+mam-dialect_central Mam
+mam-dialect_northern Mam
+mam-dialect_southern Mam
+mam-dialect_western Mam
+mqj Mamasa
+mcu Mambila, Cameroon
+mzk Mambila, Nigeria
+maw Mampruli
+mjl Mandeali
+mnk Mandinka
+mge Mango
+mbh Mangseng
+knf Mankanya
+mjv Mannan
+mbt Manobo, Matigsalug
+obo Manobo, Obo
+mbb Manobo, Western Bukidnon
+mzj Manya
+sjm Mapun
+mrw Maranao
+mar Marathi
+mpg Marba
+mhr Mari, Meadow
+enb Markweeta
+mah Marshallese
+myx Masaaba
+klv Maskelynes
+mfh Matal
+met Mato
+mcb Matsigenka
+mop Maya, Mopán
+yua Maya, Yucatec
+mfy Mayo
+maz Mazahua, Central
+vmy Mazatec, Ayautla
+maq Mazatec, Chiquihuitlán
+mzi Mazatec, Ixcatlán
+maj Mazatec, Jalapa de Díaz
+maa-dialect_sanantonio Mazatec, San Jerónimo Tecóatl
+maa-dialect_sanjerónimo Mazatec, San Jerónimo Tecóatl
+mhy Ma’anyan
+mhi Ma’di
+zmz Mbandja
+myb Mbay
+gai Mbore
+mqb Mbuko
+mbu Mbula-Bwazza
+med Melpa
+men Mende
+mee Mengen
+mwv Mentawai
+meq Merey
+zim Mesme
+mgo Meta’
+mej Meyah
+mpp Migabac
+min Minangkabau
+gum Misak
+mpx Misima-Panaeati
+mco Mixe, Coatlán
+mxq Mixe, Juquila
+pxm Mixe, Quetzaltepec
+mto Mixe, Totontepec
+mim Mixtec, Alacatlatzala
+xta Mixtec, Alcozauca
+mbz Mixtec, Amoltepec
+mip Mixtec, Apasco-Apoala
+mib Mixtec, Atatlahuca
+miy Mixtec, Ayutla
+mih Mixtec, Chayuco
+miz Mixtec, Coatzospan
+xtd Mixtec, Diuxi-Tilantongo
+mxt Mixtec, Jamiltepec
+xtm Mixtec, Magdalena Peñasco
+mxv Mixtec, Metlatónoc
+xtn Mixtec, Northern Tlaxiaco
+mie Mixtec, Ocotepec
+mil Mixtec, Peñoles
+mio Mixtec, Pinotepa Nacional
+mdv Mixtec, Santa Lucía Monteverde
+mza Mixtec, Santa María Zacatepec
+mit Mixtec, Southern Puebla
+mxb Mixtec, Tezoatlán
+mpm Mixtec, Yosondúa
+soy Miyobe
+cmo-script_latin Mnong, Central
+cmo-script_khmer Mnong, Central
+mfq Moba
+old Mochi
+mfk Mofu, North
+mif Mofu-Gudur
+mkl Mokole
+mox Molima
+myl Moma
+mqf Momuna
+mnw Mon
+mon Mongolian
+mog Mongondow
+mfe Morisyen
+mor Moro
+mqn Moronene
+mgd Moru
+mtj Moskona
+cmr Mro-Khimi
+mtd Mualang
+bmr Muinane
+moz Mukulu
+mzm Mumuye
+mnb Muna
+mnf Mundani
+unr Mundari
+fmu Muria, Far Western
+mur Murle
+tih Murut, Timugon
+muv Muthuvan
+muy Muyang
+sur Mwaghavul
+moa Mwan
+wmw Mwani
+tnr Ménik
+miq Mískito
+mos Mòoré
+muh Mündü
+nas Naasioi
+mbj Nadëb
+nfr Nafaanra
+kfw Naga, Kharam
+nst Naga, Tangshang
+nag Nagamese
+nch Nahuatl, Central Huasteca
+nhe Nahuatl, Eastern Huasteca
+ngu Nahuatl, Guerrero
+azz Nahuatl, Highland Puebla
+nhx Nahuatl, Isthmus-Mecayapan
+ncl Nahuatl, Michoacán
+nhy Nahuatl, Northern Oaxaca
+ncj Nahuatl, Northern Puebla
+nsu Nahuatl, Sierra Negra
+npl Nahuatl, Southeastern Puebla
+nuz Nahuatl, Tlamacazapa
+nhw Nahuatl, Western Huasteca
+nhi Nahuatl, Zacatlán-Ahuacatlán-Tepetzintla
+nlc Nalca
+nab Nambikuára, Southern
+gld Nanai
+nnb Nande
+npy Napu
+pbb Nasa
+ntm Nateni
+nmz Nawdm
+naw Nawuri
+nxq Naxi
+ndj Ndamba
+ndz Ndogo
+ndv Ndut
+new Newar
+nij Ngaju
+sba Ngambay
+gng Ngangam
+nga Ngbaka
+nnq Ngindo
+ngp Ngulu
+gym Ngäbere
+kdj Ng’akarimojong
+nia Nias
+nim Nilamba
+nin Ninzo
+nko Nkonya
+nog Nogai
+lem Nomaande
+not Nomatsigenga
+nhu Noone
+bud Ntcham
+nus Nuer
+yas Nugunu
+nnw Nuni, Southern
+nwb Nyabwa
+nyy Nyakyusa-Ngonde
+nyn Nyankore
+rim Nyaturu
+lid Nyindrou
+nuj Nyole
+nyo Nyoro
+nzi Nzema
+ann Obolo
+ory Odia
+ojb-script_latin Ojibwa, Northwestern
+ojb-script_syllabics Ojibwa, Northwestern
+oku Oku
+bsc Oniyan
+bdu Oroko
+orm Oromo
+ury Orya
+oss Ossetic
+ote Otomi, Mezquital
+otq Otomi, Querétaro
+stn Owa
+sig Paasaal
+kfx Pahari, Kullu
+bfz Pahari, Mahasu
+sey Paicoca
+pao Paiute, Northern
+pau Palauan
+pce Palaung, Ruching
+plw Palawano, Brooke’s Point
+pmf Pamona
+pag Pangasinan
+pap Papiamentu
+prf Paranan
+pab Parecís
+pbi Parkwa
+pbc Patamona
+pad Paumarí
+ata Pele-Ata
+pez Penan, Eastern
+peg Pengo
+fas Persian
+pcm Pidgin, Nigerian
+pis Pijin
+pny Pinyin
+pir Piratapuyo
+pjt Pitjantjatjara
+poy Pogolo
+pol Polish
+pps Popoloca, San Luís Temalacayuca
+pls Popoloca, San Marcos Tlacoyalco
+poi Popoluca, Highland
+poh-dialect_eastern Poqomchi’
+poh-dialect_western Poqomchi’
+por Portuguese
+prt Prai
+pui Puinave
+pan Punjabi, Eastern
+tsz Purepecha
+suv Puroik
+lme Pévé
+quy Quechua, Ayacucho
+qvc Quechua, Cajamarca
+quz Quechua, Cusco
+qve Quechua, Eastern Apurímac
+qub Quechua, Huallaga
+qvh Quechua, Huamalíes-Dos de Mayo Huánuco
+qwh Quechua, Huaylas Ancash
+qvw Quechua, Huaylla Wanca
+quf Quechua, Lambayeque
+qvm Quechua, Margos-Yarowilca-Lauricocha
+qul Quechua, North Bolivian
+qvn Quechua, North Junín
+qxn Quechua, Northern Conchucos Ancash
+qxh Quechua, Panao
+qvs Quechua, San Martín
+quh Quechua, South Bolivian
+qxo Quechua, Southern Conchucos
+qxr Quichua, Cañar Highland
+qvo Quichua, Napo
+qvz Quichua, Northern Pastaza
+qxl Quichua, Salasaca Highland
+quw Quichua, Tena Lowland
+kjb Q’anjob’al
+kek Q’eqchi’
+rah Rabha
+rjs Rajbanshi
+rai Ramoaaina
+lje Rampi
+rnl Ranglong
+rkt Rangpuri
+rap Rapa Nui
+yea Ravula
+raw Rawang
+rej Rejang
+rel Rendille
+ril Riang Lang
+iri Rigwe
+rgu Rikou
+rhg Rohingya
+rmc-script_latin Romani, Carpathian
+rmc-script_cyrillic Romani, Carpathian
+rmo Romani, Sinte
+rmy-script_latin Romani, Vlax
+rmy-script_cyrillic Romani, Vlax
+ron Romanian
+rol Romblomanon
+cla Ron
+rng Ronga
+rug Roviana
+run Rundi
+rus Russian
+lsm Saamya-Gwe
+spy Sabaot
+sck Sadri
+saj Sahu
+sch Sakachep
+sml Sama, Central
+xsb Sambal
+sbl Sambal, Botolan
+saq Samburu
+sbd Samo, Southern
+smo Samoan
+rav Sampang
+sxn Sangir
+sag Sango
+sbp Sangu
+xsu Sanumá
+srm Saramaccan
+sas Sasak
+apb Sa’a
+sgw Sebat Bet Gurage
+tvw Sedoa
+lip Sekpele
+slu Selaru
+snw Selee
+sea Semai
+sza Semelai
+seh Sena
+crs Seychelles French Creole
+ksb Shambala
+shn Shan
+sho Shanga
+mcd Sharanahua
+cbt Shawi
+xsr Sherpa
+shk Shilluk
+shp Shipibo-Conibo
+sna Shona
+cjs Shor
+jiv Shuar
+snp Siane
+sya Siang
+sid Sidamo
+snn Siona
+sri Siriano
+srx Sirmauri
+sil Sisaala, Tumulung
+sld Sissala
+akp Siwu
+xog Soga
+som Somali
+bmu Somba-Siawari
+khq Songhay, Koyra Chiini
+ses Songhay, Koyraboro Senni
+mnx Sougb
+spa Spanish
+srn Sranan Tongo
+sxb Suba
+suc Subanon, Western
+tgo Sudest
+suk Sukuma
+sun Sunda
+suz Sunwar
+sgj Surgujia
+sus Susu
+swh Swahili
+swe Swedish
+syl Sylheti
+dyi Sénoufo, Djimini
+myk Sénoufo, Mamara
+spp Sénoufo, Supyire
+tap Taabwa
+tby Tabaru
+tna Tacana
+shi Tachelhit
+klw Tado
+tgl Tagalog
+tbk Tagbanwa, Calamian
+tgj Tagin
+blt Tai Dam
+tbg Tairora, North
+omw Tairora, South
+tgk Tajik
+tdj Tajio
+tbc Takia
+tlj Talinga-Bwisi
+tly Talysh
+ttq-script_tifinagh Tamajaq, Tawallammat
+taj Tamang, Eastern
+taq Tamasheq
+tam Tamil
+tpm Tampulma
+tgp Tangoa
+tnn Tanna, North
+tac Tarahumara, Western
+rif-script_latin Tarifit
+rif-script_arabic Tarifit
+tat Tatar
+tav Tatuyo
+twb Tawbuid
+tbl Tboli
+kps Tehit
+twe Teiwa
+ttc Tektiteko
+tel Telugu
+kdh Tem
+tes Tengger
+tex Tennet
+tee Tepehua, Huehuetla
+tpp Tepehua, Pisaflores
+tpt Tepehua, Tlachichilco
+stp Tepehuan, Southeastern
+tfr Teribe
+twu Termanu
+ter Terêna
+tew Tewa
+tha Thai
+nod Thai, Northern
+thl Tharu, Dangaura
+tem Themne
+adx Tibetan, Amdo
+bod Tibetan, Central
+khg Tibetan, Khams
+tca Ticuna
+tir Tigrigna
+txq Tii
+tik Tikar
+dgr Tlicho
+tob Toba
+tmf Toba-Maskoy
+tng Tobanga
+tlb Tobelo
+ood Tohono O’odham
+tpi Tok Pisin
+jic Tol
+lbw Tolaki
+txa Tombonuo
+tom Tombulu
+toh Tonga
+tnt Tontemboan
+sda Toraja-Sa’dan
+tcs Torres Strait Creole
+toc Totonac, Coyutla
+tos Totonac, Highland
+neb Toura
+trn Trinitario
+trs Triqui, Chicahuaxtla
+trc Triqui, Copala
+tri Trió
+cof Tsafiki
+tkr Tsakhur
+kdl Tsikimba
+cas Tsimané
+tso Tsonga
+tuo Tucano
+iou Tuma-Irumu
+tmc Tumak
+tuf Tunebo, Central
+tur Turkish
+tuk-script_latin Turkmen
+tuk-script_arabic Turkmen
+bov Tuwuli
+tue Tuyuca
+kcg Tyap
+tzh-dialect_bachajón Tzeltal
+tzh-dialect_tenejapa Tzeltal
+tzo-dialect_chenalhó Tzotzil
+tzo-dialect_chamula Tzotzil
+tzj-dialect_western Tz’utujil
+tzj-dialect_eastern Tz’utujil
+aoz Uab Meto
+udm Udmurt
+udu Uduk
+ukr Ukrainian
+ppk Uma
+ubu Umbu-Ungu
+urk Urak Lawoi’
+ura Urarina
+urt Urat
+urd-script_devanagari Urdu
+urd-script_arabic Urdu
+urd-script_latin Urdu
+upv Uripiv-Wala-Rano-Atchin
+usp Uspanteko
+uig-script_arabic Uyghur
+uig-script_cyrillic Uyghur
+uzb-script_cyrillic Uzbek
+vag Vagla
+bav Vengo
+vid Vidunda
+vie Vietnamese
+vif Vili
+vun Vunjo
+vut Vute
+prk Wa, Parauk
+wwa Waama
+rro Waima
+bao Waimaha
+waw Waiwai
+lgl Wala
+wlx Wali
+cou Wamey
+hub Wampís
+gvc Wanano
+mfi Wandala
+wap Wapishana
+wba Warao
+war Waray-Waray
+way Wayana
+guc Wayuu
+cym Welsh
+kvw Wersing
+tnp Whitesands
+hto Witoto, Minika
+huu Witoto, Murui
+wal-script_latin Wolaytta
+wal-script_ethiopic Wolaytta
+wlo Wolio
+noa Woun Meu
+wob Wè Northern
+kao Xaasongaxango
+xer Xerénte
+yad Yagua
+yka Yakan
+sah Yakut
+yba Yala
+yli Yali, Angguruk
+nlk Yali, Ninia
+yal Yalunka
+yam Yamba
+yat Yambeta
+jmd Yamdena
+tao Yami
+yaa Yaminahua
+ame Yanesha’
+guu Yanomamö
+yao Yao
+yre Yaouré
+yva Yawa
+ybb Yemba
+pib Yine
+byr Yipma
+pil Yom
+yor Yoruba
+ycn Yucuna
+ess Yupik, Saint Lawrence Island
+yuz Yuracare
+atb Zaiwa
+zne Zande
+zaq Zapotec, Aloápam
+zpo Zapotec, Amatlán
+zad Zapotec, Cajonos
+zpc Zapotec, Choapan
+zca Zapotec, Coatecas Altas
+zpg Zapotec, Guevea de Humboldt
+zai Zapotec, Isthmus
+zpl Zapotec, Lachixío
+zam Zapotec, Miahuatlán
+zaw Zapotec, Mitla
+zpm Zapotec, Mixtepec
+zac Zapotec, Ocotlán
+zao Zapotec, Ozolotepec
+ztq Zapotec, Quioquitani-Quierí
+zar Zapotec, Rincón
+zpt Zapotec, San Vicente Coatlán
+zpi Zapotec, Santa María Quiegolani
+zas Zapotec, Santo Domingo Albarradas
+zaa Zapotec, Sierra de Juárez
+zpz Zapotec, Texmelucan
+zab Zapotec, Western Tlacolula Valley
+zpu Zapotec, Yalálag
+zae Zapotec, Yareni
+zty Zapotec, Yatee
+zav Zapotec, Yatzachi
+zza Zaza
+zyb Zhuang, Yongbei
+ziw Zigula
+zos Zoque, Francisco León
+gnd Zulgo-Gemzek
+ewe Éwé

requirements.txt

@@ -0,0 +1,11 @@
+transformers
+gradio
+Cython
+librosa
+phonemizer
+scipy
+numpy
+torch
+torchvision
+matplotlib
+Unidecode

tts.py

@@ -0,0 +1,168 @@
+import os
+import re
+import tempfile
+import torch
+import sys
+import gradio as gr
+
+from huggingface_hub import hf_hub_download
+
+# Setup TTS env
+if "vits" not in sys.path:
+    sys.path.append("vits")
+
+from vits import commons, utils
+from vits.models import SynthesizerTrn
+
+
+# TTS_LANGUAGES = {}
+# with open(f"data/tts/all_langs.tsv") as f:
+#     for line in f:
+#         iso, name = line.split(" ", 1)
+#         TTS_LANGUAGES[iso] = name
+
+
+class TextMapper(object):
+    def __init__(self, vocab_file):
+        self.symbols = [
+            x.replace("\n", "") for x in open(vocab_file, encoding="utf-8").readlines()
+        ]
+        self.SPACE_ID = self.symbols.index(" ")
+        self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
+        self._id_to_symbol = {i: s for i, s in enumerate(self.symbols)}
+
+    def text_to_sequence(self, text, cleaner_names):
+        """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+        Args:
+        text: string to convert to a sequence
+        cleaner_names: names of the cleaner functions to run the text through
+        Returns:
+        List of integers corresponding to the symbols in the text
+        """
+        sequence = []
+        clean_text = text.strip()
+        for symbol in clean_text:
+            symbol_id = self._symbol_to_id[symbol]
+            sequence += [symbol_id]
+        return sequence
+
+    def uromanize(self, text, uroman_pl):
+        iso = "xxx"
+        with tempfile.NamedTemporaryFile() as tf, tempfile.NamedTemporaryFile() as tf2:
+            with open(tf.name, "w") as f:
+                f.write("\n".join([text]))
+            cmd = f"perl " + uroman_pl
+            cmd += f" -l {iso} "
+            cmd += f" < {tf.name} > {tf2.name}"
+            os.system(cmd)
+            outtexts = []
+            with open(tf2.name) as f:
+                for line in f:
+                    line = re.sub(r"\s+", " ", line).strip()
+                    outtexts.append(line)
+            outtext = outtexts[0]
+        return outtext
+
+    def get_text(self, text, hps):
+        text_norm = self.text_to_sequence(text, hps.data.text_cleaners)
+        if hps.data.add_blank:
+            text_norm = commons.intersperse(text_norm, 0)
+        text_norm = torch.LongTensor(text_norm)
+        return text_norm
+
+    def filter_oov(self, text, lang=None):
+        text = self.preprocess_char(text, lang=lang)
+        val_chars = self._symbol_to_id
+        txt_filt = "".join(list(filter(lambda x: x in val_chars, text)))
+        return txt_filt
+
+    def preprocess_char(self, text, lang=None):
+        """
+        Special treatement of characters in certain languages
+        """
+        if lang == "ron":
+            text = text.replace("ț", "ţ")
+            print(f"{lang} (ț -> ţ): {text}")
+        return text
+
+
+def synthesize(text,speed,lang):
+    #lang = "spa"
+    #speed =1
+    if speed is None:
+        speed = 1.0
+
+    lang_code = lang.split()[0].strip()
+
+    vocab_file = hf_hub_download(
+        repo_id="facebook/mms-tts",
+        filename="vocab.txt",
+        subfolder=f"models/{lang_code}",
+    )
+    config_file = hf_hub_download(
+        repo_id="facebook/mms-tts",
+        filename="config.json",
+        subfolder=f"models/{lang_code}",
+    )
+    g_pth = hf_hub_download(
+        repo_id="facebook/mms-tts",
+        filename="G_100000.pth",
+        subfolder=f"models/{lang_code}",
+    )
+
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    elif (
+        hasattr(torch.backends, "mps")
+        and torch.backends.mps.is_available()
+        and torch.backends.mps.is_built()
+    ):
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
+
+    print(f"Run inference with {device}")
+
+    assert os.path.isfile(config_file), f"{config_file} doesn't exist"
+    hps = utils.get_hparams_from_file(config_file)
+    text_mapper = TextMapper(vocab_file)
+    net_g = SynthesizerTrn(
+        len(text_mapper.symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        **hps.model,
+    )
+    net_g.to(device)
+    _ = net_g.eval()
+
+    _ = utils.load_checkpoint(g_pth, net_g, None)
+
+    is_uroman = hps.data.training_files.split(".")[-1] == "uroman"
+
+    if is_uroman:
+        uroman_dir = "uroman"
+        assert os.path.exists(uroman_dir)
+        uroman_pl = os.path.join(uroman_dir, "bin", "uroman.pl")
+        text = text_mapper.uromanize(text, uroman_pl)
+
+    text = text.lower()
+    text = text_mapper.filter_oov(text, lang=lang)
+    stn_tst = text_mapper.get_text(text, hps)
+    with torch.no_grad():
+        x_tst = stn_tst.unsqueeze(0).to(device)
+        x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
+        hyp = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                noise_scale=0.667,
+                noise_scale_w=0.8,
+                length_scale=1.0 / speed,
+            )[0][0, 0]
+            .cpu()
+            .float()
+            .numpy()
+        )
+
+    return hps.data.sampling_rate,hyp
+    #return gr.Audio.update(value=(hps.data.sampling_rate, hyp)), text

vits/.gitignore

@@ -0,0 +1,11 @@
+DUMMY1
+DUMMY2
+DUMMY3
+logs
+__pycache__
+.ipynb_checkpoints
+.*.swp
+
+build
+*.c
+monotonic_align/monotonic_align

vits/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Jaehyeon Kim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

vits/README.md

@@ -0,0 +1,58 @@
+# VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
+
+### Jaehyeon Kim, Jungil Kong, and Juhee Son
+
+In our recent [paper](https://arxiv.org/abs/2106.06103), we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech.
+
+Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stage models. Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth.
+
+Visit our [demo](https://jaywalnut310.github.io/vits-demo/index.html) for audio samples.
+
+We also provide the [pretrained models](https://drive.google.com/drive/folders/1ksarh-cJf3F5eKJjLVWY0X1j1qsQqiS2?usp=sharing).
+
+** Update note: Thanks to [Rishikesh (ऋषिकेश)](https://github.com/jaywalnut310/vits/issues/1), our interactive TTS demo is now available on [Colab Notebook](https://colab.research.google.com/drive/1CO61pZizDj7en71NQG_aqqKdGaA_SaBf?usp=sharing).
+
+<table style="width:100%">
+  <tr>
+    <th>VITS at training</th>
+    <th>VITS at inference</th>
+  </tr>
+  <tr>
+    <td><img src="resources/fig_1a.png" alt="VITS at training" height="400"></td>
+    <td><img src="resources/fig_1b.png" alt="VITS at inference" height="400"></td>
+  </tr>
+</table>
+
+
+## Pre-requisites
+0. Python >= 3.6
+0. Clone this repository
+0. Install python requirements. Please refer [requirements.txt](requirements.txt)
+    1. You may need to install espeak first: `apt-get install espeak`
+0. Download datasets
+    1. Download and extract the LJ Speech dataset, then rename or create a link to the dataset folder: `ln -s /path/to/LJSpeech-1.1/wavs DUMMY1`
+    1. For mult-speaker setting, download and extract the VCTK dataset, and downsample wav files to 22050 Hz. Then rename or create a link to the dataset folder: `ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2`
+0. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.
+```sh
+# Cython-version Monotonoic Alignment Search
+cd monotonic_align
+python setup.py build_ext --inplace
+
+# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
+# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt 
+# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt
+```
+
+
+## Training Exmaple
+```sh
+# LJ Speech
+python train.py -c configs/ljs_base.json -m ljs_base
+
+# VCTK
+python train_ms.py -c configs/vctk_base.json -m vctk_base
+```
+
+
+## Inference Example
+See [inference.ipynb](inference.ipynb)

vits/attentions.py

@@ -0,0 +1,303 @@
+import copy
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from modules import LayerNorm
+   
+
+class Encoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+
+    self.drop = nn.Dropout(p_dropout)
+    self.attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask):
+    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.attn_layers[i](x, x, attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class Decoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+
+    self.drop = nn.Dropout(p_dropout)
+    self.self_attn_layers = nn.ModuleList()
+    self.norm_layers_0 = nn.ModuleList()
+    self.encdec_attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
+      self.norm_layers_0.append(LayerNorm(hidden_channels))
+      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask, h, h_mask):
+    """
+    x: decoder input
+    h: encoder output
+    """
+    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.self_attn_layers[i](x, x, self_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_0[i](x + y)
+
+      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+      
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class MultiHeadAttention(nn.Module):
+  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
+    super().__init__()
+    assert channels % n_heads == 0
+
+    self.channels = channels
+    self.out_channels = out_channels
+    self.n_heads = n_heads
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+    self.heads_share = heads_share
+    self.block_length = block_length
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+    self.attn = None
+
+    self.k_channels = channels // n_heads
+    self.conv_q = nn.Conv1d(channels, channels, 1)
+    self.conv_k = nn.Conv1d(channels, channels, 1)
+    self.conv_v = nn.Conv1d(channels, channels, 1)
+    self.conv_o = nn.Conv1d(channels, out_channels, 1)
+    self.drop = nn.Dropout(p_dropout)
+
+    if window_size is not None:
+      n_heads_rel = 1 if heads_share else n_heads
+      rel_stddev = self.k_channels**-0.5
+      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+    nn.init.xavier_uniform_(self.conv_q.weight)
+    nn.init.xavier_uniform_(self.conv_k.weight)
+    nn.init.xavier_uniform_(self.conv_v.weight)
+    if proximal_init:
+      with torch.no_grad():
+        self.conv_k.weight.copy_(self.conv_q.weight)
+        self.conv_k.bias.copy_(self.conv_q.bias)
+      
+  def forward(self, x, c, attn_mask=None):
+    q = self.conv_q(x)
+    k = self.conv_k(c)
+    v = self.conv_v(c)
+    
+    x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+    x = self.conv_o(x)
+    return x
+
+  def attention(self, query, key, value, mask=None):
+    # reshape [b, d, t] -> [b, n_h, t, d_k]
+    b, d, t_s, t_t = (*key.size(), query.size(2))
+    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+    if self.window_size is not None:
+      assert t_s == t_t, "Relative attention is only available for self-attention."
+      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
+      scores_local = self._relative_position_to_absolute_position(rel_logits)
+      scores = scores + scores_local
+    if self.proximal_bias:
+      assert t_s == t_t, "Proximal bias is only available for self-attention."
+      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+    if mask is not None:
+      scores = scores.masked_fill(mask == 0, -1e4)
+      if self.block_length is not None:
+        assert t_s == t_t, "Local attention is only available for self-attention."
+        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+        scores = scores.masked_fill(block_mask == 0, -1e4)
+    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
+    p_attn = self.drop(p_attn)
+    output = torch.matmul(p_attn, value)
+    if self.window_size is not None:
+      relative_weights = self._absolute_position_to_relative_position(p_attn)
+      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
+    return output, p_attn
+
+  def _matmul_with_relative_values(self, x, y):
+    """
+    x: [b, h, l, m]
+    y: [h or 1, m, d]
+    ret: [b, h, l, d]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0))
+    return ret
+
+  def _matmul_with_relative_keys(self, x, y):
+    """
+    x: [b, h, l, d]
+    y: [h or 1, m, d]
+    ret: [b, h, l, m]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+    return ret
+
+  def _get_relative_embeddings(self, relative_embeddings, length):
+    max_relative_position = 2 * self.window_size + 1
+    # Pad first before slice to avoid using cond ops.
+    pad_length = max(length - (self.window_size + 1), 0)
+    slice_start_position = max((self.window_size + 1) - length, 0)
+    slice_end_position = slice_start_position + 2 * length - 1
+    if pad_length > 0:
+      padded_relative_embeddings = F.pad(
+          relative_embeddings,
+          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+    else:
+      padded_relative_embeddings = relative_embeddings
+    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
+    return used_relative_embeddings
+
+  def _relative_position_to_absolute_position(self, x):
+    """
+    x: [b, h, l, 2*l-1]
+    ret: [b, h, l, l]
+    """
+    batch, heads, length, _ = x.size()
+    # Concat columns of pad to shift from relative to absolute indexing.
+    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
+
+    # Concat extra elements so to add up to shape (len+1, 2*len-1).
+    x_flat = x.view([batch, heads, length * 2 * length])
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
+
+    # Reshape and slice out the padded elements.
+    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
+    return x_final
+
+  def _absolute_position_to_relative_position(self, x):
+    """
+    x: [b, h, l, l]
+    ret: [b, h, l, 2*l-1]
+    """
+    batch, heads, length, _ = x.size()
+    # padd along column
+    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
+    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
+    # add 0's in the beginning that will skew the elements after reshape
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
+    return x_final
+
+  def _attention_bias_proximal(self, length):
+    """Bias for self-attention to encourage attention to close positions.
+    Args:
+      length: an integer scalar.
+    Returns:
+      a Tensor with shape [1, 1, length, length]
+    """
+    r = torch.arange(length, dtype=torch.float32)
+    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.activation = activation
+    self.causal = causal
+
+    if causal:
+      self.padding = self._causal_padding
+    else:
+      self.padding = self._same_padding
+
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+    self.drop = nn.Dropout(p_dropout)
+
+  def forward(self, x, x_mask):
+    x = self.conv_1(self.padding(x * x_mask))
+    if self.activation == "gelu":
+      x = x * torch.sigmoid(1.702 * x)
+    else:
+      x = torch.relu(x)
+    x = self.drop(x)
+    x = self.conv_2(self.padding(x * x_mask))
+    return x * x_mask
+  
+  def _causal_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = self.kernel_size - 1
+    pad_r = 0
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x
+
+  def _same_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = (self.kernel_size - 1) // 2
+    pad_r = self.kernel_size // 2
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x

vits/commons.py

@@ -0,0 +1,161 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+  classname = m.__class__.__name__
+  if classname.find("Conv") != -1:
+    m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+  return int((kernel_size*dilation - dilation)/2)
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def intersperse(lst, item):
+  result = [item] * (len(lst) * 2 + 1)
+  result[1::2] = lst
+  return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+  """KL(P||Q)"""
+  kl = (logs_q - logs_p) - 0.5
+  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
+  return kl
+
+
+def rand_gumbel(shape):
+  """Sample from the Gumbel distribution, protect from overflows."""
+  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+  return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+  return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+  ret = torch.zeros_like(x[:, :, :segment_size])
+  for i in range(x.size(0)):
+    idx_str = ids_str[i]
+    idx_end = idx_str + segment_size
+    ret[i] = x[i, :, idx_str:idx_end]
+  return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size + 1
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def get_timing_signal_1d(
+    length, channels, min_timescale=1.0, max_timescale=1.0e4):
+  position = torch.arange(length, dtype=torch.float)
+  num_timescales = channels // 2
+  log_timescale_increment = (
+      math.log(float(max_timescale) / float(min_timescale)) /
+      (num_timescales - 1))
+  inv_timescales = min_timescale * torch.exp(
+      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
+  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+  signal = F.pad(signal, [0, 0, 0, channels % 2])
+  signal = signal.view(1, channels, length)
+  return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+  return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+  n_channels_int = n_channels[0]
+  in_act = input_a + input_b
+  t_act = torch.tanh(in_act[:, :n_channels_int, :])
+  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+  acts = t_act * s_act
+  return acts
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def shift_1d(x):
+  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+  return x
+
+
+def sequence_mask(length, max_length=None):
+  if max_length is None:
+    max_length = length.max()
+  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+  return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+  """
+  duration: [b, 1, t_x]
+  mask: [b, 1, t_y, t_x]
+  """
+  device = duration.device
+  
+  b, _, t_y, t_x = mask.shape
+  cum_duration = torch.cumsum(duration, -1)
+  
+  cum_duration_flat = cum_duration.view(b * t_x)
+  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+  path = path.view(b, t_x, t_y)
+  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+  path = path.unsqueeze(1).transpose(2,3) * mask
+  return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm

vits/configs/ljs_base.json

@@ -0,0 +1,52 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 8192,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"filelists/ljs_audio_text_train_filelist.txt.cleaned",
+    "validation_files":"filelists/ljs_audio_text_val_filelist.txt.cleaned",
+    "text_cleaners":["english_cleaners2"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 0,
+    "cleaned_text": true
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false
+  }
+}

vits/configs/ljs_nosdp.json

@@ -0,0 +1,53 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 8192,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"filelists/ljs_audio_text_train_filelist.txt.cleaned",
+    "validation_files":"filelists/ljs_audio_text_val_filelist.txt.cleaned",
+    "text_cleaners":["english_cleaners2"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 0,
+    "cleaned_text": true
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "use_sdp": false
+  }
+}

vits/configs/vctk_base.json

@@ -0,0 +1,53 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 8192,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"filelists/vctk_audio_sid_text_train_filelist.txt.cleaned",
+    "validation_files":"filelists/vctk_audio_sid_text_val_filelist.txt.cleaned",
+    "text_cleaners":["english_cleaners2"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 109,
+    "cleaned_text": true
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  }
+}

vits/data_utils.py

@@ -0,0 +1,392 @@
+import time
+import os
+import random
+import numpy as np
+import torch
+import torch.utils.data
+
+import commons 
+from mel_processing import spectrogram_torch
+from utils import load_wav_to_torch, load_filepaths_and_text
+from text import text_to_sequence, cleaned_text_to_sequence
+
+
+class TextAudioLoader(torch.utils.data.Dataset):
+    """
+        1) loads audio, text pairs
+        2) normalizes text and converts them to sequences of integers
+        3) computes spectrograms from audio files.
+    """
+    def __init__(self, audiopaths_and_text, hparams):
+        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
+        self.text_cleaners  = hparams.text_cleaners
+        self.max_wav_value  = hparams.max_wav_value
+        self.sampling_rate  = hparams.sampling_rate
+        self.filter_length  = hparams.filter_length 
+        self.hop_length     = hparams.hop_length 
+        self.win_length     = hparams.win_length
+        self.sampling_rate  = hparams.sampling_rate 
+
+        self.cleaned_text = getattr(hparams, "cleaned_text", False)
+
+        self.add_blank = hparams.add_blank
+        self.min_text_len = getattr(hparams, "min_text_len", 1)
+        self.max_text_len = getattr(hparams, "max_text_len", 190)
+
+        random.seed(1234)
+        random.shuffle(self.audiopaths_and_text)
+        self._filter()
+
+
+    def _filter(self):
+        """
+        Filter text & store spec lengths
+        """
+        # Store spectrogram lengths for Bucketing
+        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
+        # spec_length = wav_length // hop_length
+
+        audiopaths_and_text_new = []
+        lengths = []
+        for audiopath, text in self.audiopaths_and_text:
+            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
+                audiopaths_and_text_new.append([audiopath, text])
+                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
+        self.audiopaths_and_text = audiopaths_and_text_new
+        self.lengths = lengths
+
+    def get_audio_text_pair(self, audiopath_and_text):
+        # separate filename and text
+        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
+        text = self.get_text(text)
+        spec, wav = self.get_audio(audiopath)
+        return (text, spec, wav)
+
+    def get_audio(self, filename):
+        audio, sampling_rate = load_wav_to_torch(filename)
+        if sampling_rate != self.sampling_rate:
+            raise ValueError("{} {} SR doesn't match target {} SR".format(
+                sampling_rate, self.sampling_rate))
+        audio_norm = audio / self.max_wav_value
+        audio_norm = audio_norm.unsqueeze(0)
+        spec_filename = filename.replace(".wav", ".spec.pt")
+        if os.path.exists(spec_filename):
+            spec = torch.load(spec_filename)
+        else:
+            spec = spectrogram_torch(audio_norm, self.filter_length,
+                self.sampling_rate, self.hop_length, self.win_length,
+                center=False)
+            spec = torch.squeeze(spec, 0)
+            torch.save(spec, spec_filename)
+        return spec, audio_norm
+
+    def get_text(self, text):
+        if self.cleaned_text:
+            text_norm = cleaned_text_to_sequence(text)
+        else:
+            text_norm = text_to_sequence(text, self.text_cleaners)
+        if self.add_blank:
+            text_norm = commons.intersperse(text_norm, 0)
+        text_norm = torch.LongTensor(text_norm)
+        return text_norm
+
+    def __getitem__(self, index):
+        return self.get_audio_text_pair(self.audiopaths_and_text[index])
+
+    def __len__(self):
+        return len(self.audiopaths_and_text)
+
+
+class TextAudioCollate():
+    """ Zero-pads model inputs and targets
+    """
+    def __init__(self, return_ids=False):
+        self.return_ids = return_ids
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text and aduio
+        PARAMS
+        ------
+        batch: [text_normalized, spec_normalized, wav_normalized]
+        """
+        # Right zero-pad all one-hot text sequences to max input length
+        _, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([x[1].size(1) for x in batch]),
+            dim=0, descending=True)
+
+        max_text_len = max([len(x[0]) for x in batch])
+        max_spec_len = max([x[1].size(1) for x in batch])
+        max_wav_len = max([x[2].size(1) for x in batch])
+
+        text_lengths = torch.LongTensor(len(batch))
+        spec_lengths = torch.LongTensor(len(batch))
+        wav_lengths = torch.LongTensor(len(batch))
+
+        text_padded = torch.LongTensor(len(batch), max_text_len)
+        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
+        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+        text_padded.zero_()
+        spec_padded.zero_()
+        wav_padded.zero_()
+        for i in range(len(ids_sorted_decreasing)):
+            row = batch[ids_sorted_decreasing[i]]
+
+            text = row[0]
+            text_padded[i, :text.size(0)] = text
+            text_lengths[i] = text.size(0)
+
+            spec = row[1]
+            spec_padded[i, :, :spec.size(1)] = spec
+            spec_lengths[i] = spec.size(1)
+
+            wav = row[2]
+            wav_padded[i, :, :wav.size(1)] = wav
+            wav_lengths[i] = wav.size(1)
+
+        if self.return_ids:
+            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
+        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
+
+
+"""Multi speaker version"""
+class TextAudioSpeakerLoader(torch.utils.data.Dataset):
+    """
+        1) loads audio, speaker_id, text pairs
+        2) normalizes text and converts them to sequences of integers
+        3) computes spectrograms from audio files.
+    """
+    def __init__(self, audiopaths_sid_text, hparams):
+        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
+        self.text_cleaners = hparams.text_cleaners
+        self.max_wav_value = hparams.max_wav_value
+        self.sampling_rate = hparams.sampling_rate
+        self.filter_length  = hparams.filter_length
+        self.hop_length     = hparams.hop_length
+        self.win_length     = hparams.win_length
+        self.sampling_rate  = hparams.sampling_rate
+
+        self.cleaned_text = getattr(hparams, "cleaned_text", False)
+
+        self.add_blank = hparams.add_blank
+        self.min_text_len = getattr(hparams, "min_text_len", 1)
+        self.max_text_len = getattr(hparams, "max_text_len", 190)
+
+        random.seed(1234)
+        random.shuffle(self.audiopaths_sid_text)
+        self._filter()
+
+    def _filter(self):
+        """
+        Filter text & store spec lengths
+        """
+        # Store spectrogram lengths for Bucketing
+        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
+        # spec_length = wav_length // hop_length
+
+        audiopaths_sid_text_new = []
+        lengths = []
+        for audiopath, sid, text in self.audiopaths_sid_text:
+            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
+                audiopaths_sid_text_new.append([audiopath, sid, text])
+                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
+        self.audiopaths_sid_text = audiopaths_sid_text_new
+        self.lengths = lengths
+
+    def get_audio_text_speaker_pair(self, audiopath_sid_text):
+        # separate filename, speaker_id and text
+        audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
+        text = self.get_text(text)
+        spec, wav = self.get_audio(audiopath)
+        sid = self.get_sid(sid)
+        return (text, spec, wav, sid)
+
+    def get_audio(self, filename):
+        audio, sampling_rate = load_wav_to_torch(filename)
+        if sampling_rate != self.sampling_rate:
+            raise ValueError("{} {} SR doesn't match target {} SR".format(
+                sampling_rate, self.sampling_rate))
+        audio_norm = audio / self.max_wav_value
+        audio_norm = audio_norm.unsqueeze(0)
+        spec_filename = filename.replace(".wav", ".spec.pt")
+        if os.path.exists(spec_filename):
+            spec = torch.load(spec_filename)
+        else:
+            spec = spectrogram_torch(audio_norm, self.filter_length,
+                self.sampling_rate, self.hop_length, self.win_length,
+                center=False)
+            spec = torch.squeeze(spec, 0)
+            torch.save(spec, spec_filename)
+        return spec, audio_norm
+
+    def get_text(self, text):
+        if self.cleaned_text:
+            text_norm = cleaned_text_to_sequence(text)
+        else:
+            text_norm = text_to_sequence(text, self.text_cleaners)
+        if self.add_blank:
+            text_norm = commons.intersperse(text_norm, 0)
+        text_norm = torch.LongTensor(text_norm)
+        return text_norm
+
+    def get_sid(self, sid):
+        sid = torch.LongTensor([int(sid)])
+        return sid
+
+    def __getitem__(self, index):
+        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
+
+    def __len__(self):
+        return len(self.audiopaths_sid_text)
+
+
+class TextAudioSpeakerCollate():
+    """ Zero-pads model inputs and targets
+    """
+    def __init__(self, return_ids=False):
+        self.return_ids = return_ids
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text, audio and speaker identities
+        PARAMS
+        ------
+        batch: [text_normalized, spec_normalized, wav_normalized, sid]
+        """
+        # Right zero-pad all one-hot text sequences to max input length
+        _, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([x[1].size(1) for x in batch]),
+            dim=0, descending=True)
+
+        max_text_len = max([len(x[0]) for x in batch])
+        max_spec_len = max([x[1].size(1) for x in batch])
+        max_wav_len = max([x[2].size(1) for x in batch])
+
+        text_lengths = torch.LongTensor(len(batch))
+        spec_lengths = torch.LongTensor(len(batch))
+        wav_lengths = torch.LongTensor(len(batch))
+        sid = torch.LongTensor(len(batch))
+
+        text_padded = torch.LongTensor(len(batch), max_text_len)
+        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
+        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+        text_padded.zero_()
+        spec_padded.zero_()
+        wav_padded.zero_()
+        for i in range(len(ids_sorted_decreasing)):
+            row = batch[ids_sorted_decreasing[i]]
+
+            text = row[0]
+            text_padded[i, :text.size(0)] = text
+            text_lengths[i] = text.size(0)
+
+            spec = row[1]
+            spec_padded[i, :, :spec.size(1)] = spec
+            spec_lengths[i] = spec.size(1)
+
+            wav = row[2]
+            wav_padded[i, :, :wav.size(1)] = wav
+            wav_lengths[i] = wav.size(1)
+
+            sid[i] = row[3]
+
+        if self.return_ids:
+            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
+        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
+
+
+class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
+    """
+    Maintain similar input lengths in a batch.
+    Length groups are specified by boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
+  
+    It removes samples which are not included in the boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
+    """
+    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
+        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+        self.lengths = dataset.lengths
+        self.batch_size = batch_size
+        self.boundaries = boundaries
+  
+        self.buckets, self.num_samples_per_bucket = self._create_buckets()
+        self.total_size = sum(self.num_samples_per_bucket)
+        self.num_samples = self.total_size // self.num_replicas
+  
+    def _create_buckets(self):
+        buckets = [[] for _ in range(len(self.boundaries) - 1)]
+        for i in range(len(self.lengths)):
+            length = self.lengths[i]
+            idx_bucket = self._bisect(length)
+            if idx_bucket != -1:
+                buckets[idx_bucket].append(i)
+  
+        for i in range(len(buckets) - 1, 0, -1):
+            if len(buckets[i]) == 0:
+                buckets.pop(i)
+                self.boundaries.pop(i+1)
+  
+        num_samples_per_bucket = []
+        for i in range(len(buckets)):
+            len_bucket = len(buckets[i])
+            total_batch_size = self.num_replicas * self.batch_size
+            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
+            num_samples_per_bucket.append(len_bucket + rem)
+        return buckets, num_samples_per_bucket
+  
+    def __iter__(self):
+      # deterministically shuffle based on epoch
+      g = torch.Generator()
+      g.manual_seed(self.epoch)
+  
+      indices = []
+      if self.shuffle:
+          for bucket in self.buckets:
+              indices.append(torch.randperm(len(bucket), generator=g).tolist())
+      else:
+          for bucket in self.buckets:
+              indices.append(list(range(len(bucket))))
+  
+      batches = []
+      for i in range(len(self.buckets)):
+          bucket = self.buckets[i]
+          len_bucket = len(bucket)
+          ids_bucket = indices[i]
+          num_samples_bucket = self.num_samples_per_bucket[i]
+  
+          # add extra samples to make it evenly divisible
+          rem = num_samples_bucket - len_bucket
+          ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
+  
+          # subsample
+          ids_bucket = ids_bucket[self.rank::self.num_replicas]
+  
+          # batching
+          for j in range(len(ids_bucket) // self.batch_size):
+              batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
+              batches.append(batch)
+  
+      if self.shuffle:
+          batch_ids = torch.randperm(len(batches), generator=g).tolist()
+          batches = [batches[i] for i in batch_ids]
+      self.batches = batches
+  
+      assert len(self.batches) * self.batch_size == self.num_samples
+      return iter(self.batches)
+  
+    def _bisect(self, x, lo=0, hi=None):
+      if hi is None:
+          hi = len(self.boundaries) - 1
+  
+      if hi > lo:
+          mid = (hi + lo) // 2
+          if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
+              return mid
+          elif x <= self.boundaries[mid]:
+              return self._bisect(x, lo, mid)
+          else:
+              return self._bisect(x, mid + 1, hi)
+      else:
+          return -1
+
+    def __len__(self):
+        return self.num_samples // self.batch_size

vits/filelists/ljs_audio_text_test_filelist.txt

@@ -0,0 +1,500 @@
+DUMMY1/LJ045-0096.wav|Mrs. De Mohrenschildt thought that Oswald,
+DUMMY1/LJ049-0022.wav|The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.
+DUMMY1/LJ033-0042.wav|Between the hours of eight and nine p.m. they were occupied with the children in the bedrooms located at the extreme east end of the house.
+DUMMY1/LJ016-0117.wav|The prisoner had nothing to deal with but wooden panels, and by dint of cutting and chopping he got both the lower panels out.
+DUMMY1/LJ025-0157.wav|Under these circumstances, unnatural as they are, with proper management, the bean will thrust forth its radicle and its plumule;
+DUMMY1/LJ042-0219.wav|Oswald demonstrated his thinking in connection with his return to the United States by preparing two sets of identical questions of the type which he might have thought
+DUMMY1/LJ032-0164.wav|it is not possible to state with scientific certainty that a particular small group of fibers come from a certain piece of clothing
+DUMMY1/LJ046-0092.wav|has confidence in the dedicated Secret Service men who are ready to lay down their lives for him
+DUMMY1/LJ050-0118.wav|Since these agencies are already obliged constantly to evaluate the activities of such groups,
+DUMMY1/LJ043-0016.wav|Jeanne De Mohrenschildt said, quote,
+DUMMY1/LJ021-0078.wav|no economic panacea, which could simply revive over-night the heavy industries and the trades dependent upon them.
+DUMMY1/LJ039-0148.wav|Examination of the cartridge cases found on the sixth floor of the Depository Building
+DUMMY1/LJ047-0202.wav|testified that the information available to the Federal Government about Oswald before the assassination would, if known to PRS,
+DUMMY1/LJ023-0056.wav|It is an easy document to understand when you remember that it was called into being
+DUMMY1/LJ021-0025.wav|And in many directions, the intervention of that organized control which we call government
+DUMMY1/LJ030-0105.wav|Communications in the motorcade.
+DUMMY1/LJ021-0012.wav|with respect to industry and business, but nearly all are agreed that private enterprise in times such as these
+DUMMY1/LJ019-0169.wav|and one or two men were allowed to mend clothes and make shoes. The rules made by the Secretary of State were hung up in conspicuous parts of the prison;
+DUMMY1/LJ039-0088.wav|It just is an aid in seeing in the fact that you only have the one element, the crosshair,
+DUMMY1/LJ016-0192.wav|"I think I could do that sort of job," said Calcraft, on the spur of the moment.
+DUMMY1/LJ014-0142.wav|was strewn in front of the dock, and sprinkled it towards the bench with a contemptuous gesture.
+DUMMY1/LJ012-0015.wav|Weedon and Lecasser to twelve and six months respectively in Coldbath Fields.
+DUMMY1/LJ048-0033.wav|Prior to November twenty-two, nineteen sixty-three
+DUMMY1/LJ028-0349.wav|who were each required to send so large a number to Babylon, that in all there were collected no fewer than fifty thousand.
+DUMMY1/LJ030-0197.wav|At first Mrs. Connally thought that her husband had been killed,
+DUMMY1/LJ017-0133.wav|Palmer speedily found imitators.
+DUMMY1/LJ034-0123.wav|Although Brennan testified that the man in the window was standing when he fired the shots, most probably he was either sitting or kneeling.
+DUMMY1/LJ003-0282.wav|Many years were to elapse before these objections should be fairly met and universally overcome.
+DUMMY1/LJ032-0204.wav|Special Agent Lyndal L. Shaneyfelt, a photography expert with the FBI,
+DUMMY1/LJ016-0241.wav|Calcraft served the city of London till eighteen seventy-four, when he was pensioned at the rate of twenty-five shillings per week.
+DUMMY1/LJ023-0033.wav|we will not allow ourselves to run around in new circles of futile discussion and debate, always postponing the day of decision.
+DUMMY1/LJ009-0286.wav|There has never been much science in the system of carrying out the extreme penalty in this country; the "finisher of the law"
+DUMMY1/LJ008-0181.wav|he had his pockets filled with bread and cheese, and it was generally supposed that he had come a long distance to see the fatal show.
+DUMMY1/LJ015-0052.wav|to the value of twenty thousand pounds.
+DUMMY1/LJ016-0314.wav|Sir George Grey thought there was a growing feeling in favor of executions within the prison precincts.
+DUMMY1/LJ047-0056.wav|From August nineteen sixty-two
+DUMMY1/LJ010-0027.wav|Nor did the methods by which they were perpetrated greatly vary from those in times past.
+DUMMY1/LJ010-0065.wav|At the former the "Provisional Government" was to be established,
+DUMMY1/LJ046-0113.wav|The Commission has concluded that at the time of the assassination
+DUMMY1/LJ028-0410.wav|There among the ruins they still live in the same kind of houses,
+DUMMY1/LJ044-0137.wav|More seriously, the facts of his defection had become known, leaving him open to almost unanswerable attack by those who opposed his views.
+DUMMY1/LJ008-0215.wav|One by one the huge uprights of black timber were fitted together,
+DUMMY1/LJ030-0084.wav|or when the press of the crowd made it impossible for the escort motorcycles to stay in position on the car's rear flanks.
+DUMMY1/LJ020-0092.wav|Have yourself called on biscuit mornings an hour earlier than usual.
+DUMMY1/LJ029-0096.wav|On November fourteen, Lawson and Sorrels attended a meeting at Love Field
+DUMMY1/LJ015-0308.wav|and others who swore to the meetings of the conspirators and their movements. Saward was found guilty,
+DUMMY1/LJ012-0067.wav|But Mrs. Solomons could not resist the temptation to dabble in stolen goods, and she was found shipping watches of the wrong category to New York.
+DUMMY1/LJ018-0231.wav|namely, to suppress it and substitute another.
+DUMMY1/LJ014-0265.wav|and later he became manager of the newly rebuilt Olympic at Wych Street.
+DUMMY1/LJ024-0102.wav|would be the first to exclaim as soon as an amendment was proposed
+DUMMY1/LJ007-0233.wav|it consists of several circular perforations, about two inches in diameter,
+DUMMY1/LJ013-0213.wav|This seems to have decided Courvoisier,
+DUMMY1/LJ032-0045.wav|This price included nineteen dollars, ninety-five cents for the rifle and the scope, and one dollar, fifty cents for postage and handling.
+DUMMY1/LJ011-0048.wav|Wherefore let him that thinketh he standeth take heed lest he fall," and was full of the most pointed allusions to the culprit.
+DUMMY1/LJ005-0294.wav|It was frequently stated in evidence that the jail of the borough was in so unfit a state for the reception of prisoners,
+DUMMY1/LJ016-0007.wav|There were others less successful.
+DUMMY1/LJ028-0138.wav|perhaps the tales that travelers told him were exaggerated as travelers' tales are likely to be,
+DUMMY1/LJ050-0029.wav|that is reflected in definite and comprehensive operating procedures.
+DUMMY1/LJ014-0121.wav|The prisoners were in due course transferred to Newgate, to be put upon their trial at the Central Criminal Court.
+DUMMY1/LJ014-0146.wav|They had to handcuff her by force against the most violent resistance, and still she raged and stormed,
+DUMMY1/LJ046-0111.wav|The Secret Service has attempted to perform this function through the activities of its Protective Research Section
+DUMMY1/LJ012-0257.wav|But the affair still remained a profound mystery. No light was thrown upon it till, towards the end of March,
+DUMMY1/LJ002-0260.wav|Yet the public opinion of the whole body seems to have checked dissipation.
+DUMMY1/LJ031-0014.wav|the Presidential limousine arrived at the emergency entrance of the Parkland Hospital at about twelve:thirty-five p.m.
+DUMMY1/LJ047-0093.wav|Oswald was arrested and jailed by the New Orleans Police Department for disturbing the peace, in connection with a street fight which broke out when he was accosted
+DUMMY1/LJ003-0324.wav|gaming of all sorts should be peremptorily forbidden under heavy pains and penalties.
+DUMMY1/LJ021-0115.wav|we have reached into the heart of the problem which is to provide such annual earnings for the lowest paid worker as will meet his minimum needs.
+DUMMY1/LJ046-0191.wav|it had established periodic regular review of the status of four hundred individuals;
+DUMMY1/LJ034-0197.wav|who was one of the first witnesses to alert the police to the Depository as the source of the shots, as has been discussed in chapter three.
+DUMMY1/LJ002-0253.wav|were governed by rules which they themselves had framed, and under which subscriptions were levied
+DUMMY1/LJ048-0288.wav|might have been more alert in the Dallas motorcade if they had retired promptly in Fort Worth.
+DUMMY1/LJ007-0112.wav|Many of the old customs once prevalent in the State Side, so properly condemned and abolished,
+DUMMY1/LJ017-0189.wav|who was presently attacked in the same way as the others, but, but, thanks to the prompt administration of remedies, he recovered.
+DUMMY1/LJ042-0230.wav|basically, although I hate the USSR and socialist system I still think marxism can work under different circumstances, end quote.
+DUMMY1/LJ050-0161.wav|The Secret Service should not and does not plan to develop its own intelligence gathering facilities to duplicate the existing facilities of other Federal agencies.
+DUMMY1/LJ003-0011.wav|that not more than one bottle of wine or one quart of beer could be issued at one time. No account was taken of the amount of liquors admitted in one day,
+DUMMY1/LJ008-0206.wav|and caused a number of stout additional barriers to be erected in front of the scaffold,
+DUMMY1/LJ002-0261.wav|The poorer prisoners were not in abject want, as in other prisons,
+DUMMY1/LJ012-0189.wav|Hunt, in consideration of the information he had given, escaped death, and was sentenced to transportation for life.
+DUMMY1/LJ019-0317.wav|The former, which consisted principally of the tread-wheel, cranks, capstans, shot-drill,
+DUMMY1/LJ011-0041.wav|Visited Mr. Fauntleroy. My application for books for him not having been attended, I had no prayer-book to give him.
+DUMMY1/LJ023-0089.wav|That is not only my accusation.
+DUMMY1/LJ044-0224.wav|would not agree with that particular wording, end quote.
+DUMMY1/LJ013-0104.wav|He found them at length residing at the latter place, one as a landed proprietor, the other as a publican.
+DUMMY1/LJ013-0055.wav|The jury did not believe him, and the verdict was for the defendants.
+DUMMY1/LJ014-0306.wav|These had been attributed to political action; some thought that the large purchases in foreign grains, effected at losing prices,
+DUMMY1/LJ029-0052.wav|To supplement the PRS files, the Secret Service depends largely on local police departments and local offices of other Federal agencies
+DUMMY1/LJ028-0459.wav|Its bricks, measuring about thirteen inches square and three inches in thickness, were burned and stamped with the usual short inscription:
+DUMMY1/LJ017-0183.wav|Soon afterwards Dixon died, showing all the symptoms already described.
+DUMMY1/LJ009-0084.wav|At length the ordinary pauses, and then, in a deep tone, which, though hardly above a whisper, is audible to all, says,
+DUMMY1/LJ007-0170.wav|That in this vast metropolis, the center of wealth, civilization, and information;
+DUMMY1/LJ016-0277.wav|This is proved by contemporary accounts, especially one graphic and realistic article which appeared in the 'Times,'
+DUMMY1/LJ009-0061.wav|He staggers towards the pew, reels into it, stumbles forward, flings himself on the ground, and, by a curious twist of the spine,
+DUMMY1/LJ019-0201.wav|to select a sufficiently spacious piece of ground, and erect a prison which from foundations to roofs should be in conformity with the newest ideas.
+DUMMY1/LJ030-0063.wav|He had repeated this wish only a few days before, during his visit to Tampa, Florida.
+DUMMY1/LJ010-0257.wav|a third miscreant made a similar but far less serious attempt in the month of July following.
+DUMMY1/LJ009-0106.wav|The keeper tries to appear unmoved, but his eye wanders anxiously over the combustible assembly.
+DUMMY1/LJ008-0121.wav|After the construction and action of the machine had been explained, the doctor asked the governor what kind of men he had commanded at Goree,
+DUMMY1/LJ050-0069.wav|the Secret Service had received from the FBI some nine thousand reports on members of the Communist Party.
+DUMMY1/LJ006-0202.wav|The news-vendor was also a tobacconist,
+DUMMY1/LJ012-0230.wav|Shortly before the day fixed for execution, Bishop made a full confession, the bulk of which bore the impress of truth,
+DUMMY1/LJ005-0248.wav|and stated that in his opinion Newgate, as the common jail of Middlesex, was wholly inadequate to the proper confinement of its prisoners.
+DUMMY1/LJ037-0053.wav|who had been greatly upset by her experience, was able to view a lineup of four men handcuffed together at the police station.
+DUMMY1/LJ045-0177.wav|For the first time
+DUMMY1/LJ004-0036.wav|it was hoped that their rulers would hire accommodation in the county prisons, and that the inferior establishments would in course of time disappear.
+DUMMY1/LJ026-0054.wav|carbohydrates (starch, cellulose) and fats.
+DUMMY1/LJ020-0085.wav|Break apart from one another and pile on a plate, throwing a clean doily or a small napkin over them. Break open at table.
+DUMMY1/LJ046-0226.wav|The several military intelligence agencies reported crank mail and similar threats involving the President.
+DUMMY1/LJ014-0233.wav|he shot an old soldier who had attempted to detain him. He was convicted and executed.
+DUMMY1/LJ033-0152.wav|The portion of the palm which was identified was the heel of the right palm, i.e., the area near the wrist, on the little finger side.
+DUMMY1/LJ004-0009.wav|as indefatigable and self-sacrificing, found by personal visitation that the condition of jails throughout the kingdom was,
+DUMMY1/LJ017-0134.wav|Within a few weeks occurred the Leeds poisoning case, in which the murderer undoubtedly was inspired by the facts made public at Palmer's trial.
+DUMMY1/LJ019-0318.wav|was to be the rule for all convicted prisoners throughout the early stages of their detention;
+DUMMY1/LJ020-0093.wav|Rise, wash face and hands, rinse the mouth out and brush back the hair.
+DUMMY1/LJ012-0188.wav|Probert was then admitted as a witness, and the case was fully proved against Thurtell, who was hanged in front of Hertford Jail.
+DUMMY1/LJ019-0202.wav|The preference given to the Pentonville system destroyed all hopes of a complete reformation of Newgate.
+DUMMY1/LJ039-0027.wav|Oswald's revolver
+DUMMY1/LJ040-0176.wav|He admitted to fantasies about being powerful and sometimes hurting and killing people, but refused to elaborate on them.
+DUMMY1/LJ018-0354.wav|Doubts were long entertained whether Thomas Wainwright,
+DUMMY1/LJ031-0185.wav|From the Presidential airplane, the Vice President telephoned Attorney General Robert F. Kennedy,
+DUMMY1/LJ006-0137.wav|They were not obliged to attend chapel, and seldom if ever went; "prisoners," said one of them under examination, "did not like the trouble of going to chapel."
+DUMMY1/LJ032-0085.wav|The Hidell signature on the notice of classification was in the handwriting of Oswald.
+DUMMY1/LJ009-0037.wav|the schoolmaster and the juvenile prisoners being seated round the communion-table, opposite the pulpit.
+DUMMY1/LJ006-0021.wav|Later on he had devoted himself to the personal investigation of the prisons of the United States.
+DUMMY1/LJ006-0082.wav|and this particular official took excellent care to select as residents for his own ward those most suitable from his own point of view.
+DUMMY1/LJ016-0380.wav|with hope to the last. There is always the chance of a flaw in the indictment, of a missing witness, or extenuating circumstances.
+DUMMY1/LJ019-0344.wav|monitor, or schoolmaster, nor to be engaged in the service of any officer of the prison.
+DUMMY1/LJ019-0161.wav|These disciplinary improvements were, however, only slowly and gradually introduced.
+DUMMY1/LJ028-0145.wav|And here I may not omit to tell the use to which the mould dug out of the great moat was turned, nor the manner wherein the wall was wrought.
+DUMMY1/LJ018-0349.wav|His disclaimer, distinct and detailed on every point, was intended simply for effect.
+DUMMY1/LJ043-0010.wav|Some of the members of that group saw a good deal of the Oswalds through the fall of nineteen sixty-three,
+DUMMY1/LJ027-0178.wav|These were undoubtedly perennibranchs. In the Permian and Triassic higher forms appeared, which were certainly caducibranch.
+DUMMY1/LJ041-0070.wav|He did not rise above the rank of private first class, even though he had passed a qualifying examination for the rank of corporal.
+DUMMY1/LJ008-0266.wav|Thus in the years between May first, eighteen twenty-seven, and thirtieth April, eighteen thirty-one,
+DUMMY1/LJ021-0091.wav|In this recent reorganization we have recognized three distinct functions:
+DUMMY1/LJ019-0129.wav|which marked the growth of public interest in prison affairs, and which was the germ of the new system
+DUMMY1/LJ018-0215.wav|William Roupell was the eldest but illegitimate son of a wealthy man who subsequently married Roupell's mother, and had further legitimate issue.
+DUMMY1/LJ015-0194.wav|and behaved so as to justify a belief that he had been a jail-bird all his life.
+DUMMY1/LJ016-0137.wav|that numbers of men, "lifers," and others with ten, fourteen, or twenty years to do, can be trusted to work out of doors without bolts and bars
+DUMMY1/LJ002-0289.wav|the latter raised eighteen pence among them to pay for a truss of straw for the poor woman to lie on.
+DUMMY1/LJ023-0016.wav|In nineteen thirty-three you and I knew that we must never let our economic system get completely out of joint again
+DUMMY1/LJ011-0141.wav|There were at the moment in Newgate six convicts sentenced to death for forging wills.
+DUMMY1/LJ016-0283.wav|to do them mere justice, there was at least till then a half-drunken ribald gaiety among the crowd that made them all akin."
+DUMMY1/LJ035-0082.wav|The only interval was the time necessary to ride in the elevator from the second to the sixth floor and walk back to the southeast corner.
+DUMMY1/LJ045-0194.wav|Anyone who was familiar with that area of Dallas would have known that the motorcade would probably pass the Texas School Book Depository to get from Main Street
+DUMMY1/LJ009-0124.wav|occupied when they saw it last, but a few hours ago, by their comrades who are now dead;
+DUMMY1/LJ030-0162.wav|In the Presidential Limousine
+DUMMY1/LJ050-0223.wav|The plan provides for an additional two hundred five agents for the Secret Service. Seventeen of this number are proposed for the Protective Research Section;
+DUMMY1/LJ008-0228.wav|their harsh and half-cracked voices full of maudlin, besotted sympathy for those about to die.
+DUMMY1/LJ002-0096.wav|The eight courts above enumerated were well supplied with water;
+DUMMY1/LJ018-0288.wav|After this the other conspirators traveled to obtain genuine bills and master the system of the leading houses at home and abroad.
+DUMMY1/LJ002-0106.wav|in which latterly a copper had been fixed for the cooking of provisions sent in by charitable persons.
+DUMMY1/LJ025-0129.wav|On each lobe of the bi-lobed leaf of Venus flytrap are three delicate filaments which stand out at right angles from the surface of the leaf.
+DUMMY1/LJ044-0013.wav|Hands Off Cuba, end quote, an application form for, and a membership card in,
+DUMMY1/LJ049-0115.wav|of the person who is actually in the exercise of the executive power, or
+DUMMY1/LJ019-0145.wav|But reformation was only skin deep. Below the surface many of the old evils still rankled.
+DUMMY1/LJ019-0355.wav|came up in all respects to modern requirements.
+DUMMY1/LJ019-0289.wav|There was unrestrained association of untried and convicted, juvenile with adult prisoners, vagrants, misdemeanants, felons.
+DUMMY1/LJ048-0222.wav|in Fort Worth, there occurred a breach of discipline by some members of the Secret Service who were officially traveling with the President.
+DUMMY1/LJ016-0367.wav|Under the new system the whole of the arrangements from first to last fell upon the officers.
+DUMMY1/LJ047-0097.wav|Agent Quigley did not know of Oswald's prior FBI record when he interviewed him,
+DUMMY1/LJ007-0075.wav|as effectually to rebuke and abash the profane spirit of the more insolent and daring of the criminals.
+DUMMY1/LJ047-0022.wav|provided by other agencies.
+DUMMY1/LJ007-0085.wav|at Newgate and York Castle as long as five years; "at Ilchester and Morpeth for seven years; at Warwick for eight years,
+DUMMY1/LJ047-0075.wav|Hosty had inquired earlier and found no evidence that it was functioning in the Dallas area.
+DUMMY1/LJ008-0098.wav|One was the "yeoman of the halter," a Newgate official, the executioner's assistant, whom Mr. J. T. Smith, who was present at the execution,
+DUMMY1/LJ017-0102.wav|The second attack was fatal, and ended in Cook's death from tetanus.
+DUMMY1/LJ046-0105.wav|Second, the adequacy of other advance preparations for the security of the President, during his visit to Dallas,
+DUMMY1/LJ018-0206.wav|He was a tall, slender man, with a long face and iron-gray hair.
+DUMMY1/LJ012-0271.wav|Whether it was greed or a quarrel that drove Greenacre to the desperate deed remains obscure.
+DUMMY1/LJ005-0086.wav|with such further separation as the justices should deem conducive to good order and discipline.
+DUMMY1/LJ042-0097.wav|and considerably better living quarters than those accorded to Soviet citizens of equal age and station.
+DUMMY1/LJ047-0126.wav|we would handle it in due course, in accord with the whole context of the investigation. End quote.
+DUMMY1/LJ041-0022.wav|Oswald first wrote, quote, Edward Vogel, end quote, an obvious misspelling of Voebel's name,
+DUMMY1/LJ015-0025.wav|The bank enjoyed an excellent reputation, it had a good connection, and was supposed to be perfectly sound.
+DUMMY1/LJ012-0194.wav|But Burke and Hare had their imitators further south,
+DUMMY1/LJ028-0416.wav|(if man may speak so confidently of His great impenetrable counsels), for an eternal Testimony of His great work in the confusion of Man's pride,
+DUMMY1/LJ007-0130.wav|are all huddled together without discrimination, oversight, or control."
+DUMMY1/LJ015-0005.wav|About this time Davidson and Gordon, the people above-mentioned,
+DUMMY1/LJ016-0125.wav|with this, placed against the wall near the chevaux-de-frise, he made an escalade.
+DUMMY1/LJ014-0224.wav|As Dwyer survived, Cannon escaped the death sentence, which was commuted to penal servitude for life.
+DUMMY1/LJ005-0019.wav|refuted by abundant evidence, and having no foundation whatever in truth.
+DUMMY1/LJ042-0221.wav|With either great ambivalence, or cold calculation he prepared completely different answers to the same questions.
+DUMMY1/LJ001-0063.wav|which was generally more formally Gothic than the printing of the German workmen,
+DUMMY1/LJ030-0006.wav|They took off in the Presidential plane, Air Force One, at eleven a.m., arriving at San Antonio at one:thirty p.m., Eastern Standard Time.
+DUMMY1/LJ024-0054.wav|democracy will have failed far beyond the importance to it of any king of precedent concerning the judiciary.
+DUMMY1/LJ006-0044.wav|the same callous indifference to the moral well-being of the prisoners, the same want of employment and of all disciplinary control.
+DUMMY1/LJ039-0154.wav|four point eight to five point six seconds if the second shot missed,
+DUMMY1/LJ050-0090.wav|they seem unduly restrictive in continuing to require some manifestation of animus against a Government official.
+DUMMY1/LJ028-0421.wav|it was the beginning of the great collections of Babylonian antiquities in the museums of the Western world.
+DUMMY1/LJ033-0205.wav|then I would say the possibility exists, these fibers could have come from this blanket, end quote.
+DUMMY1/LJ019-0335.wav|The books and journals he was to keep were minutely specified, and his constant presence in or near the jail was insisted upon.
+DUMMY1/LJ013-0045.wav|Wallace's relations warned him against his Liverpool friend,
+DUMMY1/LJ037-0002.wav|Chapter four. The Assassin: Part six.
+DUMMY1/LJ018-0159.wav|This was all the police wanted to know.
+DUMMY1/LJ026-0140.wav|In the plant as in the animal metabolism must consist of anabolic and catabolic processes.
+DUMMY1/LJ014-0171.wav|I will briefly describe one or two of the more remarkable murders in the years immediately following, then pass on to another branch of crime.
+DUMMY1/LJ037-0007.wav|Three others subsequently identified Oswald from a photograph.
+DUMMY1/LJ033-0174.wav|microscopic and UV (ultra violet) characteristics, end quote.
+DUMMY1/LJ040-0110.wav|he apparently adjusted well enough there to have had an average, although gradually deteriorating, school record
+DUMMY1/LJ039-0192.wav|he had a total of between four point eight and five point six seconds between the two shots which hit
+DUMMY1/LJ032-0261.wav|When he appeared before the Commission, Michael Paine lifted the blanket
+DUMMY1/LJ040-0097.wav|Lee was brought up in this atmosphere of constant money problems, and I am sure it had quite an effect on him, and also Robert, end quote.
+DUMMY1/LJ037-0249.wav|Mrs. Earlene Roberts, the housekeeper at Oswald's roominghouse and the last person known to have seen him before he reached tenth Street and Patton Avenue,
+DUMMY1/LJ016-0248.wav|Marwood was proud of his calling, and when questioned as to whether his process was satisfactory, replied that he heard "no complaints."
+DUMMY1/LJ004-0083.wav|As Mr. Buxton pointed out, many old acts of parliament designed to protect the prisoner were still in full force.
+DUMMY1/LJ014-0029.wav|This was Delarue's watch, fully identified as such, which Hocker told his brother Delarue had given him the morning of the murder.
+DUMMY1/LJ021-0110.wav|have been best calculated to promote industrial recovery and a permanent improvement of business and labor conditions.
+DUMMY1/LJ003-0107.wav|he slept in the same bed with a highwayman on one side, and a man charged with murder on the other.
+DUMMY1/LJ039-0076.wav|Ronald Simmons, chief of the U.S. Army Infantry Weapons Evaluation Branch of the Ballistics Research Laboratory, said, quote,
+DUMMY1/LJ016-0347.wav|had undoubtedly a solemn, impressive effect upon those outside.
+DUMMY1/LJ001-0072.wav|After the end of the fifteenth century the degradation of printing, especially in Germany and Italy,
+DUMMY1/LJ024-0018.wav|Consequently, although there never can be more than fifteen, there may be only fourteen, or thirteen, or twelve.
+DUMMY1/LJ032-0180.wav|that the fibers were caught in the crevice of the rifle's butt plate, quote, in the recent past, end quote,
+DUMMY1/LJ010-0083.wav|and measures taken to arrest them when their plans were so far developed that no doubt could remain as to their guilt.
+DUMMY1/LJ002-0299.wav|and gave the garnish for the common side at that sum, which is five shillings more than Mr. Neild says was extorted on the common side.
+DUMMY1/LJ048-0143.wav|the Secret Service did not at the time of the assassination have any established procedure governing its relationships with them.
+DUMMY1/LJ012-0054.wav|Solomons, while waiting to appear in court, persuaded the turnkeys to take him to a public-house, where all might "refresh."
+DUMMY1/LJ019-0270.wav|Vegetables, especially the potato, that most valuable anti-scorbutic, was too often omitted.
+DUMMY1/LJ035-0164.wav|three minutes after the shooting.
+DUMMY1/LJ014-0326.wav|Maltby and Co. would issue warrants on them deliverable to the importer, and the goods were then passed to be stored in neighboring warehouses.
+DUMMY1/LJ001-0173.wav|The essential point to be remembered is that the ornament, whatever it is, whether picture or pattern-work, should form part of the page,
+DUMMY1/LJ050-0056.wav|On December twenty-six, nineteen sixty-three, the FBI circulated additional instructions to all its agents,
+DUMMY1/LJ003-0319.wav|provided only that their security was not jeopardized, and dependent upon the enforcement of another new rule,
+DUMMY1/LJ006-0040.wav|The fact was that the years as they passed, nearly twenty in all, had worked but little permanent improvement in this detestable prison.
+DUMMY1/LJ017-0231.wav|His body was found lying in a pool of blood in a night-dress, stabbed over and over again in the left side.
+DUMMY1/LJ017-0226.wav|One half of the mutineers fell upon him unawares with handspikes and capstan-bars.
+DUMMY1/LJ004-0239.wav|He had been committed for an offense for which he was acquitted.
+DUMMY1/LJ048-0112.wav|The Commission also regards the security arrangements worked out by Lawson and Sorrels at Love Field as entirely adequate.
+DUMMY1/LJ039-0125.wav|that Oswald was a good shot, somewhat better than or equal to -- better than the average let us say.
+DUMMY1/LJ030-0196.wav|He cried out, quote, Oh, no, no, no. My God, they are going to kill us all, end quote,
+DUMMY1/LJ010-0228.wav|He was released from Broadmoor in eighteen seventy-eight, and went abroad.
+DUMMY1/LJ045-0228.wav|On the other hand, he could have traveled some distance with the money he did have and he did return to his room where he obtained his revolver.
+DUMMY1/LJ028-0168.wav|in the other was the sacred precinct of Jupiter Belus,
+DUMMY1/LJ021-0140.wav|and in such an effort we should be able to secure for employers and employees and consumers
+DUMMY1/LJ009-0280.wav|Again the wretched creature succeeded in obtaining foothold, but this time on the left side of the drop.
+DUMMY1/LJ003-0159.wav|To constitute this the aristocratic quarter, unwarrantable demands were made upon the space properly allotted to the female felons,
+DUMMY1/LJ016-0274.wav|and the windows of the opposite houses, which commanded a good view, as usual fetched high prices.
+DUMMY1/LJ035-0014.wav|it sounded high and I immediately kind of looked up,
+DUMMY1/LJ033-0120.wav|which he believed was where the bag reached when it was laid on the seat with one edge against the door.
+DUMMY1/LJ045-0015.wav|which Johnson said he did not receive until after the assassination. The letter said in part, quote,
+DUMMY1/LJ003-0299.wav|the latter end of the nineteenth century, several of which still fall far short of our English ideal,
+DUMMY1/LJ032-0206.wav|After comparing the rifle in the simulated photograph with the rifle in Exhibit Number one thirty-three A, Shaneyfelt testified, quote,
+DUMMY1/LJ028-0494.wav|Between the several sections were wide spaces where foot soldiers and charioteers might fight.
+DUMMY1/LJ005-0099.wav|and report at length upon the condition of the prisons of the country.
+DUMMY1/LJ015-0144.wav|developed to a colossal extent the frauds he had already practiced as a subordinate.
+DUMMY1/LJ019-0221.wav|It was intended as far as possible that, except awaiting trial, no prisoner should find himself relegated to Newgate.
+DUMMY1/LJ003-0088.wav|in one, for seven years -- that of a man sentenced to death, for whom great interest had been made, but whom it was not thought right to pardon.
+DUMMY1/LJ045-0216.wav|nineteen sixty-three, merely to disarm her and to provide a justification of sorts,
+DUMMY1/LJ042-0135.wav|that he was not yet twenty years old when he went to the Soviet Union with such high hopes and not quite twenty-three when he returned bitterly disappointed.
+DUMMY1/LJ049-0196.wav|On the other hand, it is urged that all features of the protection of the President and his family should be committed to an elite and independent corps.
+DUMMY1/LJ018-0278.wav|This was the well and astutely devised plot of the brothers Bidwell,
+DUMMY1/LJ030-0238.wav|and then looked around again and saw more of this movement, and so I proceeded to go to the back seat and get on top of him.
+DUMMY1/LJ018-0309.wav|where probably the money still remains.
+DUMMY1/LJ041-0199.wav|is shown most clearly by his employment relations after his return from the Soviet Union. Of course, he made his real problems worse to the extent
+DUMMY1/LJ007-0076.wav|The lax discipline maintained in Newgate was still further deteriorated by the presence of two other classes of prisoners who ought never to have been inmates of such a jail.
+DUMMY1/LJ039-0118.wav|He had high motivation. He had presumably a good to excellent rifle and good ammunition.
+DUMMY1/LJ024-0019.wav|And there may be only nine.
+DUMMY1/LJ008-0085.wav|The fire had not quite burnt out at twelve, in nearly four hours, that is to say.
+DUMMY1/LJ018-0031.wav|This fixed the crime pretty certainly upon Müller, who had already left the country, thus increasing suspicion under which he lay.
+DUMMY1/LJ030-0032.wav|Dallas police stood at intervals along the fence and Dallas plain clothes men mixed in the crowd.
+DUMMY1/LJ050-0004.wav|General Supervision of the Secret Service
+DUMMY1/LJ039-0096.wav|This is a definite advantage to the shooter, the vehicle moving directly away from him and the downgrade of the street, and he being in an elevated position
+DUMMY1/LJ041-0195.wav|Oswald's interest in Marxism led some people to avoid him,
+DUMMY1/LJ047-0158.wav|After a moment's hesitation, she told me that he worked at the Texas School Book Depository near the downtown area of Dallas.
+DUMMY1/LJ050-0162.wav|In planning its data processing techniques,
+DUMMY1/LJ001-0051.wav|and paying great attention to the "press work" or actual process of printing,
+DUMMY1/LJ028-0136.wav|Of all the ancient descriptions of the famous walls and the city they protected, that of Herodotus is the fullest.
+DUMMY1/LJ034-0134.wav|Shortly after the assassination Brennan noticed
+DUMMY1/LJ019-0348.wav|Every facility was promised. The sanction of the Secretary of State would not be withheld if plans and estimates were duly submitted,
+DUMMY1/LJ010-0219.wav|While one stood over the fire with the papers, another stood with lighted torch to fire the house.
+DUMMY1/LJ011-0245.wav|Mr. Mullay called again, taking with him five hundred pounds in cash. Howard discovered this, and his manner was very suspicious;
+DUMMY1/LJ030-0035.wav|Organization of the Motorcade
+DUMMY1/LJ044-0135.wav|While he had drawn some attention to himself and had actually appeared on two radio programs, he had been attacked by Cuban exiles and arrested,
+DUMMY1/LJ045-0090.wav|He was very much interested in autobiographical works of outstanding statesmen of the United States, to whom his wife thought he compared himself.
+DUMMY1/LJ026-0034.wav|When any given "protist" has to be classified the case must be decided on its individual merits;
+DUMMY1/LJ045-0092.wav|as to the fact that he was an outstanding man, end quote.
+DUMMY1/LJ017-0050.wav|Palmer, who was only thirty-one at the time of his trial, was in appearance short and stout, with a round head
+DUMMY1/LJ036-0104.wav|Whaley picked Oswald.
+DUMMY1/LJ019-0055.wav|High authorities were in favor of continuous separation.
+DUMMY1/LJ010-0030.wav|The brutal ferocity of the wild beast once aroused, the same means, the same weapons were employed to do the dreadful deed,
+DUMMY1/LJ038-0047.wav|Some of the officers saw Oswald strike McDonald with his fist. Most of them heard a click which they assumed to be a click of the hammer of the revolver.
+DUMMY1/LJ009-0074.wav|Let us pass on.
+DUMMY1/LJ048-0069.wav|Efforts made by the Bureau since the assassination, on the other hand,
+DUMMY1/LJ003-0211.wav|They were never left quite alone for fear of suicide, and for the same reason they were searched for weapons or poisons.
+DUMMY1/LJ048-0053.wav|It is the conclusion of the Commission that, even in the absence of Secret Service criteria
+DUMMY1/LJ033-0093.wav|Frazier estimated that the bag was two feet long, quote, give and take a few inches, end quote, and about five or six inches wide.
+DUMMY1/LJ006-0149.wav|The turnkeys left the prisoners very much to themselves, never entering the wards after locking-up time, at dusk, till unlocking next morning,
+DUMMY1/LJ018-0211.wav|The false coin was bought by an agent from an agent, and dealings were carried on secretly at the "Clock House" in Seven Dials.
+DUMMY1/LJ008-0054.wav|This contrivance appears to have been copied with improvements from that which had been used in Dublin at a still earlier date,
+DUMMY1/LJ040-0052.wav|that his commitment to Marxism was an important factor influencing his conduct during his adult years.
+DUMMY1/LJ028-0023.wav|Two weeks pass, and at last you stand on the eastern edge of the plateau
+DUMMY1/LJ009-0184.wav|Lord Ferrers' body was brought to Surgeons' Hall after execution in his own carriage and six;
+DUMMY1/LJ005-0252.wav|A committee was appointed, under the presidency of the Duke of Richmond
+DUMMY1/LJ015-0266.wav|has probably no parallel in the annals of crime. Saward himself is a striking and in some respects an unique figure in criminal history.
+DUMMY1/LJ017-0059.wav|even after sentence, and until within a few hours of execution, he was buoyed up with the hope of reprieve.
+DUMMY1/LJ024-0034.wav|What do they mean by the words "packing the Court"?
+DUMMY1/LJ016-0089.wav|He was engaged in whitewashing and cleaning; the officer who had him in charge left him on the stairs leading to the gallery.
+DUMMY1/LJ039-0227.wav|with two hits, within four point eight and five point six seconds.
+DUMMY1/LJ001-0096.wav|have now come into general use and are obviously a great improvement on the ordinary "modern style" in use in England, which is in fact the Bodoni type
+DUMMY1/LJ018-0129.wav|who threatened to betray the theft. But Brewer, either before or after this, succumbed to temptation,
+DUMMY1/LJ010-0157.wav|and that, as he was starving, he had resolved on this desperate deed,
+DUMMY1/LJ038-0264.wav|He concluded that, quote, the general rifling characteristics of the rifle are of the same type as those found on the bullet
+DUMMY1/LJ031-0165.wav|When security arrangements at the airport were complete, the Secret Service made the necessary arrangements for the Vice President to leave the hospital.
+DUMMY1/LJ018-0244.wav|The effect of establishing the forgeries would be to restore to the Roupell family lands for which a price had already been paid
+DUMMY1/LJ007-0071.wav|in the face of impediments confessedly discouraging
+DUMMY1/LJ028-0340.wav|Such of the Babylonians as witnessed the treachery took refuge in the temple of Jupiter Belus;
+DUMMY1/LJ017-0164.wav|with the idea of subjecting her to the irritant poison slowly but surely until the desired effect, death, was achieved.
+DUMMY1/LJ048-0197.wav|I then told the officers that their primary duty was traffic and crowd control and that they should be alert for any persons who might attempt to throw anything
+DUMMY1/LJ013-0098.wav|Mr. Oxenford having denied that he had made any transfer of stock, the matter was at once put into the hands of the police.
+DUMMY1/LJ012-0049.wav|led him to think seriously of trying his fortunes in another land.
+DUMMY1/LJ030-0014.wav|quote, that the crowd was about the same as the one which came to see him before but there were one hundred thousand extra people on hand who came to see Mrs. Kennedy.
+DUMMY1/LJ014-0186.wav|A milliner's porter,
+DUMMY1/LJ015-0027.wav|Yet even so early as the death of the first Sir John Paul,
+DUMMY1/LJ047-0049.wav|Marina Oswald, however, recalled that her husband was upset by this interview.
+DUMMY1/LJ012-0021.wav|at fourteen he was a pickpocket and a "duffer," or a seller of sham goods.
+DUMMY1/LJ003-0140.wav|otherwise he would have been stripped of his clothes. End quote.
+DUMMY1/LJ042-0130.wav|Shortly thereafter, less than eighteen months after his defection, about six weeks before he met Marina Prusakova,
+DUMMY1/LJ019-0180.wav|His letter to the Corporation, under date fourth June,
+DUMMY1/LJ017-0108.wav|He was struck with the appearance of the corpse, which was not emaciated, as after a long disease ending in death;
+DUMMY1/LJ006-0268.wav|Women saw men if they merely pretended to be wives; even boys were visited by their sweethearts.
+DUMMY1/LJ044-0125.wav|of residence in the U.S.S.R. against any cause which I join, by association,
+DUMMY1/LJ015-0231.wav|It was Tester's business, who had access to the railway company's books, to watch for this.
+DUMMY1/LJ002-0225.wav|The rentals of rooms and fees went to the warden, whose income was two thousand three hundred seventy-two pounds.
+DUMMY1/LJ034-0072.wav|The employees raced the elevators to the first floor. Givens saw Oswald standing at the gate on the fifth floor as the elevator went by.
+DUMMY1/LJ045-0033.wav|He began to treat me better. He helped me more -- although he always did help. But he was more attentive, end quote.
+DUMMY1/LJ031-0058.wav|to infuse blood and fluids into the circulatory system.
+DUMMY1/LJ029-0197.wav|During November the Dallas papers reported frequently on the plans for protecting the President, stressing the thoroughness of the preparations.
+DUMMY1/LJ043-0047.wav|Oswald and his family lived for a brief period with his mother at her urging, but Oswald soon decided to move out.
+DUMMY1/LJ021-0026.wav|seems necessary to produce the same result of justice and right conduct
+DUMMY1/LJ003-0230.wav|The prison allowances were eked out by the broken victuals generously given by several eating-house keepers in the city,
+DUMMY1/LJ037-0252.wav|Ted Callaway, who saw the gunman moments after the shooting, testified that Commission Exhibit Number one sixty-two
+DUMMY1/LJ031-0008.wav|Meanwhile, Chief Curry ordered the police base station to notify Parkland Hospital that the wounded President was en route.
+DUMMY1/LJ030-0021.wav|all one had to do was get a high building someday with a telescopic rifle, and there was nothing anybody could do to defend against such an attempt.
+DUMMY1/LJ046-0179.wav|being reviewed regularly.
+DUMMY1/LJ025-0118.wav|and that, however diverse may be the fabrics or tissues of which their bodies are composed, all these varied structures result
+DUMMY1/LJ028-0278.wav|Zopyrus, when they told him, not thinking that it could be true, went and saw the colt with his own eyes;
+DUMMY1/LJ007-0090.wav|Not only did their presence tend greatly to interfere with the discipline of the prison, but their condition was deplorable in the extreme.
+DUMMY1/LJ045-0045.wav|that she would be able to leave the Soviet Union. Marina Oswald has denied this.
+DUMMY1/LJ028-0289.wav|For he cut off his own nose and ears, and then, clipping his hair close and flogging himself with a scourge,
+DUMMY1/LJ009-0276.wav|Calcraft, the moment he had adjusted the cap and rope, ran down the steps, drew the bolt, and disappeared.
+DUMMY1/LJ031-0122.wav|treated the gunshot wound in the left thigh.
+DUMMY1/LJ016-0205.wav|he received a retaining fee of five pounds, five shillings, with the usual guinea for each job;
+DUMMY1/LJ019-0248.wav|leading to an inequality, uncertainty, and inefficiency of punishment productive of the most prejudicial results.
+DUMMY1/LJ033-0183.wav|it was not surprising that the replica sack made on December one, nineteen sixty-three,
+DUMMY1/LJ037-0001.wav|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
+DUMMY1/LJ018-0218.wav|In eighteen fifty-five
+DUMMY1/LJ001-0102.wav|Here and there a book is printed in France or Germany with some pretension to good taste,
+DUMMY1/LJ007-0125.wav|It was diverted from its proper uses, and, as the "place of the greatest comfort," was allotted to persons who should not have been sent to Newgate at all.
+DUMMY1/LJ050-0022.wav|A formal and thorough description of the responsibilities of the advance agent is now in preparation by the Service.
+DUMMY1/LJ028-0212.wav|On the night of the eleventh day Gobrias killed the son of the King.
+DUMMY1/LJ028-0357.wav|yet we may be sure that Babylon was taken by Darius only by use of stratagem. Its walls were impregnable.
+DUMMY1/LJ014-0199.wav|there was no case to make out; why waste money on lawyers for the defense? His demeanor was cool and collected throughout;
+DUMMY1/LJ016-0077.wav|A man named Lears, under sentence of transportation for an attempt at murder on board ship, got up part of the way,
+DUMMY1/LJ009-0194.wav|and that executors or persons having lawful possession of the bodies
+DUMMY1/LJ014-0094.wav|Discovery of the murder came in this wise. O'Connor, a punctual and well-conducted official, was at once missed at the London Docks.
+DUMMY1/LJ001-0079.wav|Caslon's type is clear and neat, and fairly well designed;
+DUMMY1/LJ026-0052.wav|In the nutrition of the animal the most essential and characteristic part of the food supply is derived from vegetable
+DUMMY1/LJ013-0005.wav|One of the earliest of the big operators in fraudulent finance was Edward Beaumont Smith,
+DUMMY1/LJ033-0072.wav|I then stepped off of it and the officer picked it up in the middle and it bent so.
+DUMMY1/LJ036-0067.wav|According to McWatters, the Beckley bus was behind the Marsalis bus, but he did not actually see it.
+DUMMY1/LJ025-0098.wav|and it is probable that amyloid substances are universally present in the animal organism, though not in the precise form of starch.
+DUMMY1/LJ005-0257.wav|during which time a host of witnesses were examined, and the committee presented three separate reports,
+DUMMY1/LJ004-0024.wav|Thus in eighteen thirteen the exaction of jail fees had been forbidden by law,
+DUMMY1/LJ049-0154.wav|In eighteen ninety-four,
+DUMMY1/LJ039-0059.wav|(three) his experience and practice after leaving the Marine Corps, and (four) the accuracy of the weapon and the quality of the ammunition.
+DUMMY1/LJ007-0150.wav|He is allowed intercourse with prostitutes who, in nine cases out of ten, have originally conduced to his ruin;
+DUMMY1/LJ015-0001.wav|Chronicles of Newgate, Volume two. By Arthur Griffiths. Section eighteen: Newgate notorieties continued, part three.
+DUMMY1/LJ010-0158.wav|feeling, as he said, that he might as well be shot or hanged as remain in such a state.
+DUMMY1/LJ010-0281.wav|who had borne the Queen's commission, first as cornet, and then lieutenant, in the tenth Hussars.
+DUMMY1/LJ033-0055.wav|and he could disassemble it more rapidly.
+DUMMY1/LJ015-0218.wav|A new accomplice was now needed within the company's establishment, and Pierce looked about long before he found the right person.
+DUMMY1/LJ027-0006.wav|In all these lines the facts are drawn together by a strong thread of unity.
+DUMMY1/LJ016-0049.wav|He had here completed his ascent.
+DUMMY1/LJ006-0088.wav|It was not likely that a system which left innocent men -- for the great bulk of new arrivals were still untried
+DUMMY1/LJ042-0133.wav|a great change must have occurred in Oswald's thinking to induce him to return to the United States.
+DUMMY1/LJ045-0234.wav|While he did become enraged at at least one point in his interrogation,
+DUMMY1/LJ046-0033.wav|The adequacy of existing procedures can fairly be assessed only after full consideration of the difficulty of the protective assignment,
+DUMMY1/LJ037-0061.wav|and having, quote, somewhat bushy, end quote, hair.
+DUMMY1/LJ032-0025.wav|the officers of Klein's discovered that a rifle bearing serial number C two seven six six had been shipped to one A. Hidell,
+DUMMY1/LJ047-0197.wav|in view of all the information concerning Oswald in its files, should have alerted the Secret Service to Oswald's presence in Dallas
+DUMMY1/LJ018-0130.wav|and stole paper on a much larger scale than Brown.
+DUMMY1/LJ005-0265.wav|It was recommended that the dietaries should be submitted and approved like the rules; that convicted prisoners should not receive any food but the jail allowance;
+DUMMY1/LJ044-0105.wav|He presented Arnold Johnson, Gus Hall,
+DUMMY1/LJ015-0043.wav|This went on for some time, and might never have been discovered had some good stroke of luck provided any of the partners
+DUMMY1/LJ030-0125.wav|On several occasions when the Vice President's car was slowed down by the throng, Special Agent Youngblood stepped out to hold the crowd back.
+DUMMY1/LJ043-0140.wav|He also studied Dallas bus schedules to prepare for his later use of buses to travel to and from General Walker's house.
+DUMMY1/LJ002-0220.wav|In consequence of these disclosures, both Bambridge and Huggin, his predecessor in the office, were committed to Newgate,
+DUMMY1/LJ034-0117.wav|At one:twenty-nine p.m. the police radio reported
+DUMMY1/LJ018-0276.wav|The first plot was against Mr. Harry Emmanuel, but he escaped, and the attempt was made upon Loudon and Ryder.
+DUMMY1/LJ004-0077.wav|nor has he a right to poison or starve his fellow-creatures."
+DUMMY1/LJ042-0194.wav|they should not be confused with slowness, indecision or fear. Only the intellectually fearless could even be remotely attracted to our doctrine,
+DUMMY1/LJ029-0114.wav|The route chosen from the airport to Main Street was the normal one, except where Harwood Street was selected as the means of access to Main Street
+DUMMY1/LJ014-0194.wav|The policemen were now in possession;
+DUMMY1/LJ032-0027.wav|According to its microfilm records, Klein's received an order for a rifle on March thirteen, nineteen sixty-three,
+DUMMY1/LJ048-0289.wav|However, there is no evidence that these men failed to take any action in Dallas within their power that would have averted the tragedy.
+DUMMY1/LJ043-0188.wav|that he was the leader of a fascist organization, and when I said that even though all of that might be true, just the same he had no right to take his life,
+DUMMY1/LJ011-0118.wav|In eighteen twenty-nine the gallows claimed two more victims for this offense.
+DUMMY1/LJ040-0201.wav|After her interview with Mrs. Oswald,
+DUMMY1/LJ033-0056.wav|While the rifle may have already been disassembled when Oswald arrived home on Thursday, he had ample time that evening to disassemble the rifle
+DUMMY1/LJ047-0073.wav|Hosty considered the information to be, quote, stale, unquote, by that time, and did not attempt to verify Oswald's reported statement.
+DUMMY1/LJ001-0153.wav|only nominally so, however, in many cases, since when he uses a headline he counts that in,
+DUMMY1/LJ007-0158.wav|or any kind of moral improvement was impossible; the prisoner's career was inevitably downward, till he struck the lowest depths.
+DUMMY1/LJ028-0502.wav|The Ishtar gateway leading to the palace was encased with beautiful blue glazed bricks,
+DUMMY1/LJ028-0226.wav|Though Herodotus wrote nearly a hundred years after Babylon fell, his story seems to bear the stamp of truth.
+DUMMY1/LJ010-0038.wav|as there had been before; as in the year eighteen forty-nine, a year memorable for the Rush murders at Norwich,
+DUMMY1/LJ019-0241.wav|But in the interval very comprehensive and, I think it must be admitted, salutary changes were successively introduced into the management of prisons.
+DUMMY1/LJ001-0094.wav|were induced to cut punches for a series of "old style" letters.
+DUMMY1/LJ001-0015.wav|the forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves.
+DUMMY1/LJ047-0015.wav|From defection to return to Fort Worth.
+DUMMY1/LJ044-0139.wav|since there was no background to the New Orleans FPCC, quote, organization, end quote, which consisted solely of Oswald.
+DUMMY1/LJ050-0031.wav|that the Secret Service consciously set about the task of inculcating and maintaining the highest standard of excellence and esprit, for all of its personnel.
+DUMMY1/LJ050-0235.wav|It has also used other Federal law enforcement agents during Presidential visits to cities in which such agents are stationed.
+DUMMY1/LJ050-0137.wav|FBI, and the Secret Service.
+DUMMY1/LJ031-0109.wav|At one:thirty-five p.m., after Governor Connally had been moved to the operating room, Dr. Shaw started the first operation
+DUMMY1/LJ031-0041.wav|He noted that the President was blue-white or ashen in color; had slow, spasmodic, agonal respiration without any coordination;
+DUMMY1/LJ021-0139.wav|There should be at least a full and fair trial given to these means of ending industrial warfare;
+DUMMY1/LJ029-0004.wav|The narrative of these events is based largely on the recollections of the participants,
+DUMMY1/LJ023-0122.wav|It was said in last year's Democratic platform,
+DUMMY1/LJ005-0264.wav|inspectors of prisons should be appointed, who should visit all the prisons from time to time and report to the Secretary of State.
+DUMMY1/LJ002-0105.wav|and beyond it was a room called the "wine room," because formerly used for the sale of wine, but
+DUMMY1/LJ017-0035.wav|in the interests and for the due protection of the public, that the fullest and fairest inquiry should be made,
+DUMMY1/LJ048-0252.wav|Three of these agents occupied positions on the running boards of the car, and the fourth was seated in the car.
+DUMMY1/LJ013-0109.wav|The proceeds of the robbery were lodged in a Boston bank,
+DUMMY1/LJ039-0139.wav|Oswald obtained a hunting license, joined a hunting club and went hunting about six times, as discussed more fully in chapter six.
+DUMMY1/LJ044-0047.wav|that anyone ever attacked any street demonstration in which Oswald was involved, except for the Bringuier incident mentioned above,
+DUMMY1/LJ016-0417.wav|Catherine Wilson, the poisoner, was reserved and reticent to the last, expressing no contrition, but also no fear --
+DUMMY1/LJ045-0178.wav|he left his wedding ring in a cup on the dresser in his room. He also left one hundred seventy dollars in a wallet in one of the dresser drawers.
+DUMMY1/LJ009-0172.wav|While in London, for instance, in eighteen twenty-nine, twenty-four persons had been executed for crimes other than murder,
+DUMMY1/LJ049-0202.wav|incident to its responsibilities.
+DUMMY1/LJ032-0103.wav|The name "Hidell" was stamped on some of the "Chapter's" printed literature and on the membership application blanks.
+DUMMY1/LJ013-0091.wav|and Elder had to be assisted by two bank porters, who carried it for him to a carriage waiting near the Mansion House.
+DUMMY1/LJ037-0208.wav|nineteen dollars, ninety-five cents, plus one dollar, twenty-seven cents shipping charge, had been collected from the consignee, Hidell.
+DUMMY1/LJ014-0128.wav|her hair was dressed in long crepe bands. She had lace ruffles at her wrist, and wore primrose-colored kid gloves.
+DUMMY1/LJ015-0007.wav|This affected Cole's credit, and ugly reports were in circulation charging him with the issue of simulated warrants.
+DUMMY1/LJ036-0169.wav|he would have reached his destination at approximately twelve:fifty-four p.m.
+DUMMY1/LJ021-0040.wav|The second step we have taken in the restoration of normal business enterprise
+DUMMY1/LJ015-0036.wav|The bank was already insolvent,
+DUMMY1/LJ034-0041.wav|Although Bureau experiments had shown that twenty-four hours was a likely maximum time, Latona stated
+DUMMY1/LJ009-0192.wav|The dissection of executed criminals was abolished soon after the discovery of the crime of burking,
+DUMMY1/LJ037-0248.wav|The eyewitnesses vary in their identification of the jacket.
+DUMMY1/LJ015-0289.wav|As each transaction was carried out from a different address, and a different messenger always employed,
+DUMMY1/LJ005-0072.wav|After a few years of active exertion the Society was rewarded by fresh legislation.
+DUMMY1/LJ023-0047.wav|The three horses are, of course, the three branches of government -- the Congress, the Executive and the courts.
+DUMMY1/LJ009-0126.wav|Hardly any one.
+DUMMY1/LJ034-0097.wav|The window was approximately one hundred twenty feet away.
+DUMMY1/LJ028-0462.wav|They were laid in bitumen.
+DUMMY1/LJ046-0055.wav|It is now possible for Presidents to travel the length and breadth of a land far larger than the United States
+DUMMY1/LJ019-0371.wav|Yet the law was seldom if ever enforced.
+DUMMY1/LJ039-0207.wav|Although all of the shots were a few inches high and to the right of the target,
+DUMMY1/LJ002-0174.wav|Mr. Buxton's friends at once paid the forty shillings, and the boy was released.
+DUMMY1/LJ016-0233.wav|In his own profession
+DUMMY1/LJ026-0108.wav|It is clear that there are upward and downward currents of water containing food (comparable to blood of an animal),
+DUMMY1/LJ038-0035.wav|Oswald rose from his seat, bringing up both hands.
+DUMMY1/LJ026-0148.wav|water which is lost by evaporation, especially from the leaf surface through the stomata;
+DUMMY1/LJ001-0186.wav|the position of our Society that a work of utility might be also a work of art, if we cared to make it so.
+DUMMY1/LJ016-0264.wav|The upturned faces of the eager spectators resembled those of the 'gods' at Drury Lane on Boxing Night;
+DUMMY1/LJ009-0041.wav|The occupants of this terrible black pew were the last always to enter the chapel.
+DUMMY1/LJ010-0297.wav|But there were other notorious cases of forgery.
+DUMMY1/LJ040-0018.wav|the Commission is not able to reach any definite conclusions as to whether or not he was, quote, sane, unquote, under prevailing legal standards.
+DUMMY1/LJ005-0253.wav|"to inquire into and report upon the several jails and houses of correction in the counties, cities, and corporate towns within England and Wales
+DUMMY1/LJ027-0176.wav|Fishes first appeared in the Devonian and Upper Silurian in very reptilian or rather amphibian forms.
+DUMMY1/LJ034-0035.wav|The position of this palmprint on the carton was parallel with the long axis of the box, and at right angles with the short axis;
+DUMMY1/LJ016-0054.wav|But he did not like the risk of entering a room by the fireplace, and the chances of detection it offered.
+DUMMY1/LJ018-0262.wav|Roupell received the announcement with a cheerful countenance,
+DUMMY1/LJ044-0237.wav|with thirteen dollars, eighty-seven cents when considerably greater resources were available to him.
+DUMMY1/LJ034-0166.wav|Two other witnesses were able to offer partial descriptions of a man they saw in the southeast corner window
+DUMMY1/LJ016-0238.wav|"just to steady their legs a little;" in other words, to add his weight to that of the hanging bodies.
+DUMMY1/LJ042-0198.wav|The discussion above has already set forth examples of his expression of hatred for the United States.
+DUMMY1/LJ031-0189.wav|At two:thirty-eight p.m., Eastern Standard Time, Lyndon Baines Johnson took the oath of office as the thirty-sixth President of the United States.
+DUMMY1/LJ050-0084.wav|or, quote, other high government officials in the nature of a complaint coupled with an expressed or implied determination to use a means,
+DUMMY1/LJ044-0158.wav|As for my return entrance visa please consider it separately. End quote.
+DUMMY1/LJ045-0082.wav|it appears that Marina Oswald also complained that her husband was not able to provide more material things for her.
+DUMMY1/LJ045-0190.wav|appeared in The Dallas Times Herald on November fifteen, nineteen sixty-three.
+DUMMY1/LJ035-0155.wav|The only exit from the office in the direction Oswald was moving was through the door to the front stairway.
+DUMMY1/LJ044-0004.wav|Political Activities
+DUMMY1/LJ046-0016.wav|The Commission has not undertaken a comprehensive examination of all facets of this subject;
+DUMMY1/LJ019-0368.wav|The latter too was to be laid before the House of Commons.
+DUMMY1/LJ010-0062.wav|But they proceeded in all seriousness, and would have shrunk from no outrage or atrocity in furtherance of their foolhardy enterprise.
+DUMMY1/LJ033-0159.wav|It was from Oswald's right hand, in which he carried the long package as he walked from Frazier's car to the building.
+DUMMY1/LJ002-0171.wav|The boy declared he saw no one, and accordingly passed through without paying the toll of a penny.
+DUMMY1/LJ002-0298.wav|in his evidence in eighteen fourteen, said it was more,
+DUMMY1/LJ012-0219.wav|and in one corner, at some depth, a bundle of clothes were unearthed, which, with a hairy cap,
+DUMMY1/LJ017-0190.wav|After this came the charge of administering oil of vitriol, which failed, as has been described.
+DUMMY1/LJ019-0179.wav|This, with a scheme for limiting the jail to untried prisoners, had been urgently recommended by Lord John Russell in eighteen thirty.
+DUMMY1/LJ050-0188.wav|each patrolman might be given a prepared booklet of instructions explaining what is expected of him. The Secret Service has expressed concern
+DUMMY1/LJ006-0043.wav|The disgraceful overcrowding had been partially ended, but the same evils of indiscriminate association were still present; there was the old neglect of decency,
+DUMMY1/LJ029-0060.wav|A number of people who resembled some of those in the photographs were placed under surveillance at the Trade Mart.
+DUMMY1/LJ019-0052.wav|Both systems came to us from the United States. The difference was really more in degree than in principle,
+DUMMY1/LJ037-0081.wav|Later in the day each woman found an empty shell on the ground near the house. These two shells were delivered to the police.
+DUMMY1/LJ048-0200.wav|paying particular attention to the crowd for any unusual activity.
+DUMMY1/LJ016-0426.wav|come along, gallows.
+DUMMY1/LJ008-0182.wav|A tremendous crowd assembled when Bellingham was executed in eighteen twelve for the murder of Spencer Percival, at that time prime minister;
+DUMMY1/LJ043-0107.wav|Upon moving to New Orleans on April twenty-four, nineteen sixty-three,
+DUMMY1/LJ006-0084.wav|and so numerous were his opportunities of showing favoritism, that all the prisoners may be said to be in his power.
+DUMMY1/LJ025-0081.wav|has no permanent digestive cavity or mouth, but takes in its food anywhere and digests, so to speak, all over its body.
+DUMMY1/LJ019-0042.wav|These were either satisfied with a makeshift, and modified existing buildings, without close regard to their suitability, or for a long time did nothing at all.
+DUMMY1/LJ047-0240.wav|They agree that Hosty told Revill
+DUMMY1/LJ032-0012.wav|the resistance to arrest and the attempted shooting of another police officer by the man (Lee Harvey Oswald) subsequently accused of assassinating President Kennedy
+DUMMY1/LJ050-0209.wav|The assistant to the Director of the FBI testified that

vits/filelists/ljs_audio_text_test_filelist.txt.cleaned

@@ -0,0 +1,500 @@
+DUMMY1/LJ045-0096.wav|mɪsˈɛs də mˈoʊɹɪnstʃˌaɪlt θˈɔːt ðæt ˈɑːswəld,
+DUMMY1/LJ049-0022.wav|ðə sˈiːkɹət sˈɜːvɪs bɪlˈiːvd ðˌɐɾɪt wʌz vˈɛɹi dˈaʊtfəl ðæt ˌɛni pɹˈɛzɪdənt wʊd ɹˈaɪd ɹˈɛɡjuːlɚli ɪn ɐ vˈiəkəl wɪð ɐ fˈɪkst tˈɑːp, ˈiːvən ðˌoʊ tɹænspˈæɹənt.
+DUMMY1/LJ033-0042.wav|bɪtwˌiːn ðɪ ˈaɪʊɹz ʌv ˈeɪt ænd nˈaɪn pˈiː.ˈɛm. ðeɪ wɜːɹ ˈɑːkjʊpˌaɪd wɪððə tʃˈɪldɹən ɪnðə bˈɛdɹuːmz loʊkˈeɪɾᵻd æt ðɪ ɛkstɹˈiːm ˈiːst ˈɛnd ʌvðə hˈaʊs.
+DUMMY1/LJ016-0117.wav|ðə pɹˈɪzənɚ hɐd nˈʌθɪŋ tə dˈiːl wɪð bˌʌt wˈʊdən pˈænəlz, ænd baɪ dˈɪnt ʌv kˈʌɾɪŋ ænd tʃˈɑːpɪŋ hiː ɡɑːt bˈoʊθ ðə lˈoʊɚ pˈænəlz ˈaʊt.
+DUMMY1/LJ025-0157.wav|ˌʌndɚ ðiːz sˈɜːkəmstˌænsᵻz, ʌnnˈætʃɚɹəl æz ðeɪ ɑːɹ, wɪð pɹˈɑːpɚ mˈænɪdʒmənt, ðə bˈiːn wɪl θɹˈʌst fˈɔːɹθ ɪts ɹˈædɪkəl ænd ɪts plˈuːmjuːl;
+DUMMY1/LJ042-0219.wav|ˈɑːswəld dˈɛmənstɹˌeɪɾᵻd hɪz θˈɪŋkɪŋ ɪn kənˈɛkʃən wɪð hɪz ɹɪtˈɜːn tə ðə juːnˈaɪɾᵻd stˈeɪts baɪ pɹɪpˈɛɹɹɪŋ tˈuː sˈɛts ʌv aɪdˈɛntɪkəl kwˈɛstʃənz ʌvðə tˈaɪp wˌɪtʃ hiː mˌaɪthɐv θˈɔːt
+DUMMY1/LJ032-0164.wav|ɪt ɪz nˌɑːt pˈɑːsəbəl tə stˈeɪt wɪð saɪəntˈɪfɪk sˈɜːtənti ðˌæɾə pɚtˈɪkjʊlɚ smˈɔːl ɡɹˈuːp ʌv fˈaɪbɚz kˈʌm fɹʌm ɐ sˈɜːtən pˈiːs ʌv klˈoʊðɪŋ
+DUMMY1/LJ046-0092.wav|hɐz kˈɑːnfɪdəns ɪnðə dˈɛdᵻkˌeɪɾᵻd sˈiːkɹət sˈɜːvɪs mˈɛn hˌuː ɑːɹ ɹˈɛdi tə lˈeɪ dˌaʊn ðɛɹ lˈaɪvz fɔːɹ hˌɪm
+DUMMY1/LJ050-0118.wav|sˈɪns ðiːz ˈeɪdʒənsiz ɑːɹ ɔːlɹˌɛdi əblˈaɪdʒd kˈɑːnstəntli tʊ ɪvˈæljuːˌeɪt ðɪ æktˈɪvɪɾiz ʌv sˈʌtʃ ɡɹˈuːps,
+DUMMY1/LJ043-0016.wav|dʒˈiːn də mˈoʊɹɪnstʃˌaɪlt sˈɛd, kwˈoʊt,
+DUMMY1/LJ021-0078.wav|nˈoʊ ˌiːkənˈɑːmɪk pˌænɐsˈiːə, wˌɪtʃ kʊd sˈɪmpli ɹɪvˈaɪv ˌoʊvɚnˈaɪt ðə hˈɛvi ˈɪndʌstɹˌɪz ænd ðə tɹˈeɪdz dɪpˈɛndənt əpˌɑːn ðˌɛm.
+DUMMY1/LJ039-0148.wav|ɛɡzˌæmᵻnˈeɪʃən ʌvðə kˈɑːɹtɹɪdʒ kˈeɪsᵻz fˈaʊnd ɑːnðə sˈɪksθ flˈoːɹ ʌvðə dɪpˈɑːsɪtˌoːɹi bˈɪldɪŋ
+DUMMY1/LJ047-0202.wav|tˈɛstɪfˌaɪd ðætðɪ ˌɪnfɚmˈeɪʃən ɐvˈeɪləbəl tə ðə fˈɛdɚɹəl ɡˈʌvɚnmənt ɐbˌaʊt ˈɑːswəld bɪfˌoːɹ ðɪ ɐsˌæsᵻnˈeɪʃən wˈʊd, ɪf nˈoʊn tə pˌiːˌɑːɹˈɛs,
+DUMMY1/LJ023-0056.wav|ɪt ɪz ɐn ˈiːzi dˈɑːkjuːmənt tʊ ˌʌndɚstˈænd wɛn juː ɹɪmˈɛmbɚ ðˌɐɾɪt wʌz kˈɔːld ˌɪntʊ bˈiːɪŋ
+DUMMY1/LJ021-0025.wav|ænd ɪn mˈɛni dɚɹˈɛkʃənz, ðɪ ˌɪntɚvˈɛnʃən ʌv ðæt ˈɔːɹɡɐnˌaɪzd kəntɹˈoʊl wˌɪtʃ wiː kˈɔːl ɡˈʌvɚnmənt
+DUMMY1/LJ030-0105.wav|kəmjˌuːnɪkˈeɪʃənz ɪnðə mˈoʊɾɚkˌeɪd.
+DUMMY1/LJ021-0012.wav|wɪð ɹɪspˈɛkt tʊ ˈɪndʌstɹi ænd bˈɪznəs, bˌʌt nˌɪɹli ˈɔːl ɑːɹ ɐɡɹˈiːd ðæt pɹˈaɪvət ˈɛntɚpɹˌaɪz ɪn tˈaɪmz sˈʌtʃ ɐz ðˈiːz
+DUMMY1/LJ019-0169.wav|ænd wˈʌn ɔːɹ tˈuː mˈɛn wɜːɹ ɐlˈaʊd tə mˈɛnd klˈoʊðz ænd mˌeɪk ʃˈuːz. ðə ɹˈuːlz mˌeɪd baɪ ðə sˈɛkɹətɹi ʌv stˈeɪt wɜː hˈʌŋ ˌʌp ɪn kənspˈɪkjuːəs pˈɑːɹts ʌvðə pɹˈɪzən;
+DUMMY1/LJ039-0088.wav|ɪt dʒˈʌst ɪz ɐn ˈeɪd ɪn sˈiːɪŋ ɪnðə fˈækt ðæt juː ˈoʊnli hæv ðə wˈʌn ˈɛlɪmənt, ðə kɹˈɔshɛɹ,
+DUMMY1/LJ016-0192.wav|"ˈaɪ θˈɪŋk ˈaɪ kʊd dˈuː ðæt sˈɔːɹt ʌv dʒˈɑːb," sˈɛd kˈælkɹæft, ɑːnðə spˈɜːɹ ʌvðə mˈoʊmənt.
+DUMMY1/LJ014-0142.wav|wʌz stɹˈuːn ɪn fɹˈʌnt ʌvðə dˈɑːk, ænd spɹˈɪŋkəld ɪt tʊwˈɔːɹdz ðə bˈɛntʃ wɪð ɐ kəntˈɛmptʃuːəs dʒˈɛstʃɚ.
+DUMMY1/LJ012-0015.wav|wˈiːdɑːn ænd lˈɛkəsɚ tə twˈɛlv ænd sˈɪks mˈʌnθs ɹɪspˈɛktɪvli ɪn kˈoʊldbæθ fˈiːldz.
+DUMMY1/LJ048-0033.wav|pɹˈaɪɚ tə noʊvˈɛmbɚ twˈɛntitˈuː, naɪntˈiːn sˈɪkstiθɹˈiː
+DUMMY1/LJ028-0349.wav|hˌuː wɜːɹ ˈiːtʃ ɹɪkwˈaɪɚd tə sˈɛnd sˌoʊ lˈɑːɹdʒ ɐ nˈʌmbɚ tə bˈæbɪlən, ðæt ɪn ˈɔːl ðɛɹwˌɜː kəlˈɛktᵻd nˈoʊ fjˈuːɚ ðɐn fˈɪfti θˈaʊzənd.
+DUMMY1/LJ030-0197.wav|æt fˈɜːst mɪsˈɛs kənˈæli θˈɔːt ðæt hɜː hˈʌsbənd hɐdbɪn kˈɪld,
+DUMMY1/LJ017-0133.wav|pˈɑːmɚ spˈiːdɪli fˈaʊnd ˈɪmᵻtˌeɪɾɚz.
+DUMMY1/LJ034-0123.wav|ɑːlðˈoʊ bɹˈɛnən tˈɛstɪfˌaɪd ðætðə mˈæn ɪnðə wˈɪndoʊ wʌz stˈændɪŋ wɛn hiː fˈaɪɚd ðə ʃˈɑːts, mˈoʊst pɹˈɑːbəbli hiː wʌz ˈiːðɚ sˈɪɾɪŋ ɔːɹ nˈiːlɪŋ.
+DUMMY1/LJ003-0282.wav|mˈɛni jˈɪɹz wɜː tʊ ɪlˈæps bɪfˌoːɹ ðiːz ɑːbdʒˈɛkʃənz ʃˌʊd biː fˈɛɹli mˈɛt ænd jˌuːnɪvˈɜːsəli ˌoʊvɚkˈʌm.
+DUMMY1/LJ032-0204.wav|spˈɛʃəl ˈeɪdʒənt lˈɪndəl ˈɛl. ʃˈeɪnaɪfəlt, ɐ fətˈɑːɡɹəfi ˈɛkspɜːt wɪððɪ ˌɛfbˌiːˈaɪ,
+DUMMY1/LJ016-0241.wav|kˈælkɹæft sˈɜːvd ðə sˈɪɾi ʌv lˈʌndən tˈɪl eɪtˈiːn sˈɛvəntifˈoːɹ, wˌɛn hiː wʌz pˈɛnʃənd æt ðə ɹˈeɪt ʌv twˈɛntifˈaɪv ʃˈɪlɪŋz pɜː wˈiːk.
+DUMMY1/LJ023-0033.wav|wiː wɪl nˌɑːt ɐlˈaʊ aɪʊɹsˈɛlvz tə ɹˈʌn ɐɹˈaʊnd ɪn nˈuː sˈɜːkəlz ʌv fjˈuːɾəl dɪskˈʌʃən ænd dɪbˈeɪt, ˈɔːlweɪz poʊstpˈoʊnɪŋ ðə dˈeɪ ʌv dᵻsˈɪʒən.
+DUMMY1/LJ009-0286.wav|ðɛɹ hɐz nˈɛvɚ bˌɪn mˈʌtʃ sˈaɪəns ɪnðə sˈɪstəm ʌv kˈæɹɪɪŋ ˈaʊt ðɪ ɛkstɹˈiːm pˈɛnəlɾi ɪn ðɪs kˈʌntɹi; ðˈə "fˈɪnɪʃɚɹ ʌvðə lˈɔː"
+DUMMY1/LJ008-0181.wav|hiː hɐd hɪz pˈɑːkɪts fˈɪld wɪð bɹˈɛd ænd tʃˈiːz, ænd ɪt wʌz dʒˈɛnɚɹəli sʌpˈoʊzd ðæt hiː hɐd kˈʌm ɐ lˈɑːŋ dˈɪstəns tə sˈiː ðə fˈeɪɾəl ʃˈoʊ.
+DUMMY1/LJ015-0052.wav|tə ðə vˈæljuː ʌv twˈɛnti θˈaʊzənd pˈaʊndz.
+DUMMY1/LJ016-0314.wav|sˌɜː dʒˈɔːɹdʒ ɡɹˈeɪ θˈɔːt ðɛɹwˌʌz ɐ ɡɹˈoʊɪŋ fˈiːlɪŋ ɪn fˈeɪvɚɹ ʌv ˌɛksɪkjˈuːʃənz wɪðˌɪn ðə pɹˈɪzən pɹˈiːsɪŋkts.
+DUMMY1/LJ047-0056.wav|fɹʌm ˈɔːɡəst naɪntˈiːn sˈɪkstitˈuː
+DUMMY1/LJ010-0027.wav|nˈɔːɹ dˈɪd ðə mˈɛθədz baɪ wˌɪtʃ ðeɪ wɜː pˈɜːpɪtɹˌeɪɾᵻd ɡɹˈeɪtli vˈɛɹi fɹʌm ðoʊz ɪn tˈaɪmz pˈæst.
+DUMMY1/LJ010-0065.wav|æt ðə fˈɔːɹmɚ ðə "pɹəvˈɪʒənəl ɡˈʌvɚnmənt" wʌz təbi ɪstˈæblɪʃt,
+DUMMY1/LJ046-0113.wav|ðə kəmˈɪʃən hɐz kənklˈuːdᵻd ðæt æt ðə tˈaɪm ʌvðɪ ɐsˌæsᵻnˈeɪʃən
+DUMMY1/LJ028-0410.wav|ðɛɹ ɐmˌʌŋ ðə ɹˈuːɪnz ðeɪ stˈɪl lˈɪv ɪnðə sˈeɪm kˈaɪnd ʌv hˈaʊzɪz,
+DUMMY1/LJ044-0137.wav|mˈoːɹ sˈiəɹɪəsli, ðə fˈækts ʌv hɪz dɪfˈɛkʃən hɐd bɪkˌʌm nˈoʊn, lˈiːvɪŋ hˌɪm ˈoʊpən tʊ ˈɔːlmoʊst ʌnˈænsɚɹəbəl ɐtˈæk baɪ ðoʊz hˌuː əpˈoʊzd hɪz vjˈuːz.
+DUMMY1/LJ008-0215.wav|wˈʌn baɪ wˈʌn ðə hjˈuːdʒ ˈʌpɹaɪts ʌv blˈæk tˈɪmbɚ wɜː fˈɪɾᵻd təɡˈɛðɚ,
+DUMMY1/LJ030-0084.wav|ɔːɹ wɛn ðə pɹˈɛs ʌvðə kɹˈaʊd mˌeɪd ɪt ɪmpˈɑːsəbəl fɚðɪ ˈɛskɔːɹt mˈoʊɾɚsˌaɪkəlz tə stˈeɪ ɪn pəzˈɪʃən ɑːnðə kˈɑːɹz ɹˈɪɹ flˈæŋks.
+DUMMY1/LJ020-0092.wav|hæv joːɹsˈɛlf kˈɔːld ˌɑːn bˈɪskɪt mˈɔːɹnɪŋz ɐn ˈaɪʊɹ ˈɜːlɪɚ ðɐn jˈuːʒuːəl.
+DUMMY1/LJ029-0096.wav|ˌɑːn noʊvˈɛmbɚ foːɹtˈiːn, lˈɔːsən ænd sˈɔːɹəlz ɐtˈɛndᵻd ɐ mˈiːɾɪŋ æt lˈʌv fˈiːld
+DUMMY1/LJ015-0308.wav|ænd ˈʌðɚz hˌuː swˈoːɹ tə ðə mˈiːɾɪŋz ʌvðə kənspˈɪɹəɾɚz ænd ðɛɹ mˈuːvmənts. sˈæwɚd wʌz fˈaʊnd ɡˈɪlti,
+DUMMY1/LJ012-0067.wav|bˌʌt mɪsˈɛs sˈɑːlɑːmənz kʊd nˌɑːt ɹɪsˈɪst ðə tɛmptˈeɪʃən tə dˈæbəl ɪn stˈoʊlən ɡˈʊdz, ænd ʃiː wʌz fˈaʊnd ʃˈɪpɪŋ wˈɑːtʃᵻz ʌvðə ɹˈɔŋ kˈæɾɪɡɚɹi tə nˈuː jˈɔːɹk.
+DUMMY1/LJ018-0231.wav|nˈeɪmli, tə səpɹˈɛs ɪt ænd sˈʌbstɪtˌuːt ɐnˈʌðɚ.
+DUMMY1/LJ014-0265.wav|ænd lˈeɪɾɚ hiː bɪkˌeɪm mˈænɪdʒɚɹ ʌvðə nˈuːli ɹɪbˈɪlt əlˈɪmpɪk æt wˈɪtʃ stɹˈiːt.
+DUMMY1/LJ024-0102.wav|wʊd biː ðə fˈɜːst tʊ ɛksklˈeɪm æz sˈuːn æz ɐn ɐmˈɛndmənt wʌz pɹəpˈoʊzd
+DUMMY1/LJ007-0233.wav|ɪt kənsˈɪsts ʌv sˈɛvɹəl sˈɜːkjʊlɚ pˌɜːfɚɹˈeɪʃənz, ɐbˌaʊt tˈuː ˈɪntʃᵻz ɪn daɪˈæmɪɾɚ,
+DUMMY1/LJ013-0213.wav|ðɪs sˈiːmz tə hæv dᵻsˈaɪdᵻd kˈɜːvɔɪzɪɚ,
+DUMMY1/LJ032-0045.wav|ðɪs pɹˈaɪs ɪnklˈuːdᵻd naɪntˈiːn dˈɑːlɚz, nˈaɪntifˈaɪv sˈɛnts fɚðə ɹˈaɪfəl ænd ðə skˈoʊp, ænd wˈʌn dˈɑːlɚ, fˈɪfti sˈɛnts fɔːɹ pˈoʊstɪdʒ ænd hˈændlɪŋ.
+DUMMY1/LJ011-0048.wav|wˈɛɹfoːɹ lˈɛt hˌɪm ðæt θˈɪŋkəθ hiː stˈændəθ tˈeɪk hˈiːd lˈɛst hiː fˈɔːl," ænd wʌz fˈʊl ʌvðə mˈoʊst pˈɔɪntᵻd ɐlˈuːʒənz tə ðə kˈʌlpɹɪt.
+DUMMY1/LJ005-0294.wav|ɪt wʌz fɹˈiːkwəntli stˈeɪɾᵻd ɪn ˈɛvɪdəns ðætðə dʒˈeɪl ʌvðə bˈʌɹoʊ wʌz ɪn sˌoʊ ʌnfˈɪt ɐ stˈeɪt fɚðə ɹɪsˈɛpʃən ʌv pɹˈɪzənɚz,
+DUMMY1/LJ016-0007.wav|ðɛɹwˌɜːɹ ˈʌðɚz lˈɛs səksˈɛsfəl.
+DUMMY1/LJ028-0138.wav|pɚhˈæps ðə tˈeɪlz ðæt tɹˈævəlɚz tˈoʊld hˌɪm wɜːɹ ɛɡzˈædʒɚɹˌeɪɾᵻd æz tɹˈævəlɚz tˈeɪlz ɑːɹ lˈaɪkli tə bˈiː,
+DUMMY1/LJ050-0029.wav|ðæt ɪz ɹɪflˈɛktᵻd ɪn dˈɛfɪnət ænd kˌɑːmpɹɪhˈɛnsɪv ˈɑːpɚɹˌeɪɾɪŋ pɹəsˈiːdʒɚz.
+DUMMY1/LJ014-0121.wav|ðə pɹˈɪzənɚz wɜːɹ ɪn dˈuː kˈoːɹs tɹænsfˈɜːd tə nˈuːɡeɪt, təbi pˌʊt əpˌɑːn ðɛɹ tɹˈaɪəl æt ðə sˈɛntɹəl kɹˈɪmɪnəl kˈoːɹt.
+DUMMY1/LJ014-0146.wav|ðeɪ hædtə hˈændkʌf hɜː baɪ fˈoːɹs ɐɡˈɛnst ðə mˈoʊst vˈaɪələnt ɹɪsˈɪstəns, ænd stˈɪl ʃiː ɹˈeɪdʒd ænd stˈoːɹmd,
+DUMMY1/LJ046-0111.wav|ðə sˈiːkɹət sˈɜːvɪs hɐz ɐtˈɛmptᵻd tə pɚfˈɔːɹm ðɪs fˈʌŋkʃən θɹuː ðɪ æktˈɪvɪɾiz ʌv ɪts pɹətˈɛktɪv ɹɪsˈɜːtʃ sˈɛkʃən
+DUMMY1/LJ012-0257.wav|bˌʌt ðɪ ɐfˈɛɹ stˈɪl ɹɪmˈeɪnd ɐ pɹəfˈaʊnd mˈɪstɚɹi. nˈoʊ lˈaɪt wʌz θɹˈoʊn əpˌɑːn ɪt tˈɪl, tʊwˈɔːɹdz ðɪ ˈɛnd ʌv mˈɑːɹtʃ,
+DUMMY1/LJ002-0260.wav|jˈɛt ðə pˈʌblɪk əpˈɪniən ʌvðə hˈoʊl bˈɑːdi sˈiːmz tə hæv tʃˈɛkt dˌɪsɪpˈeɪʃən.
+DUMMY1/LJ031-0014.wav|ðə pɹˌɛzɪdˈɛnʃəl lˈɪməzˌiːn ɐɹˈaɪvd æt ðɪ ɪmˈɜːdʒənsi ˈɛntɹəns ʌvðə pˈɑːɹklənd hˈɑːspɪɾəl æt ɐbˌaʊt twˈɛlv:θˈɜːɾifˈaɪv pˈiː.ˈɛm.
+DUMMY1/LJ047-0093.wav|ˈɑːswəld wʌz ɐɹˈɛstᵻd ænd dʒˈeɪld baɪ ðə nˈuː ˈɔːɹliənz pəlˈiːs dɪpˈɑːɹtmənt fɔːɹ dɪstˈɜːbɪŋ ðə pˈiːs, ɪn kənˈɛkʃən wɪð ɐ stɹˈiːt fˈaɪt wˌɪtʃ bɹˈoʊk ˈaʊt wɛn hiː wʌz ɐkˈɔstᵻd
+DUMMY1/LJ003-0324.wav|ɡˈeɪmɪŋ ʌv ˈɔːl sˈɔːɹts ʃˌʊd biː pˈɛɹɪmptˌoːɹəli fəbˈɪdən ˌʌndɚ hˈɛvi pˈeɪnz ænd pˈɛnəlɾɪz.
+DUMMY1/LJ021-0115.wav|wiː hæv ɹˈiːtʃt ˌɪntʊ ðə hˈɑːɹt ʌvðə pɹˈɑːbləm wˌɪtʃ ɪz tə pɹəvˈaɪd sˈʌtʃ ˈænjuːəl ˈɜːnɪŋz fɚðə lˈoʊəst pˈeɪd wˈɜːkɚɹ æz wɪl mˈiːt hɪz mˈɪnɪməm nˈiːdz.
+DUMMY1/LJ046-0191.wav|ɪt hɐd ɪstˈæblɪʃt pˌiəɹɪˈɑːdɪk ɹˈɛɡjuːlɚ ɹɪvjˈuː ʌvðə stˈæɾəs ʌv fˈoːɹ hˈʌndɹəd ˌɪndɪvˈɪdʒuːəlz;
+DUMMY1/LJ034-0197.wav|hˌuː wʌz wˈʌn ʌvðə fˈɜːst wˈɪtnəsᵻz tʊ ɐlˈɜːt ðə pəlˈiːs tə ðə dɪpˈɑːsɪtˌoːɹi æz ðə sˈoːɹs ʌvðə ʃˈɑːts, æz hɐzbɪn dɪskˈʌst ɪn tʃˈæptɚ θɹˈiː.
+DUMMY1/LJ002-0253.wav|wɜː ɡˈʌvɚnd baɪ ɹˈuːlz wˌɪtʃ ðeɪ ðɛmsˈɛlvz hɐd fɹˈeɪmd, ænd ˌʌndɚ wˌɪtʃ sʌbskɹˈɪpʃənz wɜː lˈɛvɪd
+DUMMY1/LJ048-0288.wav|mˌaɪthɐv bˌɪn mˈoːɹ ɐlˈɜːt ɪnðə dˈæləs mˈoʊɾɚkˌeɪd ɪf ðeɪ hɐd ɹɪtˈaɪɚd pɹˈɑːmptli ɪn fˈɔːɹt wˈɜːθ.
+DUMMY1/LJ007-0112.wav|mˈɛnɪəv ðɪ ˈoʊld kˈʌstəmz wˈʌns pɹˈɛvələnt ɪnðə stˈeɪt sˈaɪd, sˌoʊ pɹˈɑːpɚli kəndˈɛmd ænd ɐbˈɑːlɪʃt,
+DUMMY1/LJ017-0189.wav|hˌuː wʌz pɹˈɛzəntli ɐtˈækt ɪnðə sˈeɪm wˈeɪ æz ðɪ ˈʌðɚz, bˈʌt, bˈʌt, θˈæŋks tə ðə pɹˈɑːmpt ɐdmˌɪnɪstɹˈeɪʃən ʌv ɹˈɛmədiz, hiː ɹɪkˈʌvɚd.
+DUMMY1/LJ042-0230.wav|bˈeɪsɪkli, ɑːlðˈoʊ ˈaɪ hˈeɪt ðə jˌuːˌɛsˌɛsˈɑːɹ ænd sˈoʊʃəlˌɪst sˈɪstəm ˈaɪ stˈɪl θˈɪŋk mˈɑːɹksɪzəm kæn wˈɜːk ˌʌndɚ dˈɪfɹənt sˈɜːkəmstˌænsᵻz, ˈɛnd kwˈoʊt.
+DUMMY1/LJ050-0161.wav|ðə sˈiːkɹət sˈɜːvɪs ʃˌʊd nˌɑːt ænd dʌznˌɑːt plˈæn tə dɪvˈɛləp ɪts ˈoʊn ɪntˈɛlɪdʒəns ɡˈæðɚɹɪŋ fəsˈɪlɪɾiz tə dˈuːplᵻkˌeɪt ðɪ ɛɡzˈɪstɪŋ fəsˈɪlɪɾiz ʌv ˈʌðɚ fˈɛdɚɹəl ˈeɪdʒənsiz.
+DUMMY1/LJ003-0011.wav|ðæt nˌɑːt mˈoːɹ ðɐn wˈʌn bˈɑːɾəl ʌv wˈaɪn ɔːɹ wˈʌn kwˈɔːɹt ʌv bˈɪɹ kʊd biː ˈɪʃuːd æt wˈʌn tˈaɪm. nˈoʊ ɐkˈaʊnt wʌz tˈeɪkən ʌvðɪ ɐmˈaʊnt ʌv lˈɪkɚz ɐdmˈɪɾᵻd ɪn wˈʌn dˈeɪ,
+DUMMY1/LJ008-0206.wav|ænd kˈɔːzd ɐ nˈʌmbɚɹ ʌv stˈaʊt ɐdˈɪʃənəl bˈæɹɪɹz təbi ɪɹˈɛktᵻd ɪn fɹˈʌnt ʌvðə skˈæfoʊld,
+DUMMY1/LJ002-0261.wav|ðə pˈʊɹɚ pɹˈɪzənɚz wɜː nˌɑːt ɪn ˈæbdʒɛkt wˈɑːnt, æz ɪn ˈʌðɚ pɹˈɪzənz,
+DUMMY1/LJ012-0189.wav|hˈʌnt, ɪn kənsˌɪdɚɹˈeɪʃən ʌvðɪ ˌɪnfɚmˈeɪʃən hiː hɐd ɡˈɪvən, ɛskˈeɪpt dˈɛθ, ænd wʌz sˈɛntənst tə tɹænspoːɹtˈeɪʃən fɔːɹ lˈaɪf.
+DUMMY1/LJ019-0317.wav|ðə fˈɔːɹmɚ, wˌɪtʃ kənsˈɪstᵻd pɹˈɪnsɪpəli ʌvðə tɹˈɛdwˈiːl, kɹˈæŋks, kˈæpstənz, ʃˈɑːtdɹˈɪl,
+DUMMY1/LJ011-0041.wav|vˈɪzɪɾᵻd mˈɪstɚ fˈɔːntəlɹˌɔɪ. maɪ ˌæplɪkˈeɪʃən fɔːɹ bˈʊks fɔːɹ hˌɪm nˌɑːt hˌævɪŋ bˌɪn ɐtˈɛndᵻd, ˈaɪ hɐd nˈoʊ pɹˈɛɹbˈʊk tə ɡˈɪv hˌɪm.
+DUMMY1/LJ023-0089.wav|ðæt ɪz nˌɑːt ˈoʊnli maɪ ˌækjuːzˈeɪʃən.
+DUMMY1/LJ044-0224.wav|wʊd nˌɑːt ɐɡɹˈiː wɪð ðæt pɚtˈɪkjʊlɚ wˈɜːdɪŋ, ˈɛnd kwˈoʊt.
+DUMMY1/LJ013-0104.wav|hiː fˈaʊnd ðˌɛm æt lˈɛŋθ ɹɪsˈaɪdɪŋ æt ðə lˈæɾɚ plˈeɪs, wˈʌn æz ɐ lˈændᵻd pɹəpɹˈaɪəɾɚ, ðɪ ˈʌðɚɹ æz ɐ pˈʌblɪkən.
+DUMMY1/LJ013-0055.wav|ðə dʒˈʊɹi dɪdnˌɑːt bɪlˈiːv hˌɪm, ænd ðə vˈɜːdɪkt wʌz fɚðə dɪfˈɛndənts.
+DUMMY1/LJ014-0306.wav|ðiːz hɐdbɪn ɐtɹˈɪbjuːɾᵻd tə pəlˈɪɾɪkəl ˈækʃən; sˌʌm θˈɔːt ðætðə lˈɑːɹdʒ pˈɜːtʃɪsᵻz ɪn fˈɔːɹən ɡɹˈeɪnz, ɪfˈɛktᵻd æt lˈuːzɪŋ pɹˈaɪsᵻz,
+DUMMY1/LJ029-0052.wav|tə sˈʌplɪmənt ðə pˌiːˌɑːɹˈɛs fˈaɪlz, ðə sˈiːkɹət sˈɜːvɪs dɪpˈɛndz lˈɑːɹdʒli ˌɑːn lˈoʊkəl pəlˈiːs dɪpˈɑːɹtmənts ænd lˈoʊkəl ˈɑːfɪsᵻz ʌv ˈʌðɚ fˈɛdɚɹəl ˈeɪdʒənsiz
+DUMMY1/LJ028-0459.wav|ɪts bɹˈɪks, mˈɛʒɚɹɪŋ ɐbˌaʊt θɜːtˈiːn ˈɪntʃᵻz skwˈɛɹ ænd θɹˈiː ˈɪntʃᵻz ɪn θˈɪknəs, wɜː bˈɜːnd ænd stˈæmpt wɪððə jˈuːʒuːəl ʃˈɔːɹt ɪnskɹˈɪpʃən:
+DUMMY1/LJ017-0183.wav|sˈuːn ˈæftɚwɚdz dˈɪksən dˈaɪd, ʃˈoʊɪŋ ˈɔːl ðə sˈɪmptəmz ɔːlɹˌɛdi dɪskɹˈaɪbd.
+DUMMY1/LJ009-0084.wav|æt lˈɛŋθ ðɪ ˈɔːɹdɪnˌɛɹi pˈɔːzᵻz, ænd ðˈɛn, ɪn ɐ dˈiːp tˈoʊn, wˈɪtʃ, ðˌoʊ hˈɑːɹdli əbˌʌv ɐ wˈɪspɚ, ɪz ˈɔːdəbəl tʊ ˈɔːl, sˈɛz,
+DUMMY1/LJ007-0170.wav|ðæt ɪn ðɪs vˈæst mətɹˈɑːpəlˌɪs, ðə sˈɛntɚɹ ʌv wˈɛlθ, sˌɪvɪlaɪzˈeɪʃən, ænd ˌɪnfɚmˈeɪʃən;
+DUMMY1/LJ016-0277.wav|ðɪs ɪz pɹˈuːvd baɪ kəntˈɛmpɚɹˌɛɹi ɐkˈaʊnts, ɪspˈɛʃəli wˈʌn ɡɹˈæfɪk ænd ɹiːəlˈɪstɪk ˈɑːɹɾɪkəl wˌɪtʃ ɐpˈɪɹd ɪnðə tˈaɪmz,
+DUMMY1/LJ009-0061.wav|hiː stˈæɡɚz tʊwˈɔːɹdz ðə pjˈuː, ɹˈiːlz ˌɪntʊ ɪt, stˈʌmbəlz fˈoːɹwɚd, flˈɪŋz hɪmsˈɛlf ɑːnðə ɡɹˈaʊnd, ˈænd, baɪ ɐ kjˈʊɹɪəs twˈɪst ʌvðə spˈaɪn,
+DUMMY1/LJ019-0201.wav|tə sɪlˈɛkt ɐ səfˈɪʃəntli spˈeɪʃəs pˈiːs ʌv ɡɹˈaʊnd, ænd ɪɹˈɛkt ɐ pɹˈɪzən wˌɪtʃ fɹʌm faʊndˈeɪʃənz tə ɹˈuːfs ʃˌʊd biː ɪn kənfˈoːɹmɪɾi wɪððə nˈuːəst aɪdˈiəz.
+DUMMY1/LJ030-0063.wav|hiː hɐd ɹɪpˈiːɾᵻd ðɪs wˈɪʃ ˈoʊnli ɐ fjˈuː dˈeɪz bɪfˈoːɹ, dˈʊɹɪŋ hɪz vˈɪzɪt tə tˈæmpə, flˈɔːɹɪdə.
+DUMMY1/LJ010-0257.wav|ɐ θˈɜːd mˈɪskɹiənt mˌeɪd ɐ sˈɪmɪlɚ bˌʌt fˈɑːɹ lˈɛs sˈiəɹɪəs ɐtˈɛmpt ɪnðə mˈʌnθ ʌv dʒuːlˈaɪ fˈɑːloʊɪŋ.
+DUMMY1/LJ009-0106.wav|ðə kˈiːpɚ tɹˈaɪz tʊ ɐpˈɪɹ ʌnmˈuːvd, bˌʌt hɪz ˈaɪ wˈɑːndɚz ˈæŋʃəsli ˌoʊvɚ ðə kəmbˈʌstəbəl ɐsˈɛmbli.
+DUMMY1/LJ008-0121.wav|ˈæftɚ ðə kənstɹˈʌkʃən ænd ˈækʃən ʌvðə məʃˈiːn hɐdbɪn ɛksplˈeɪnd, ðə dˈɑːktɚɹ ˈæskt ðə ɡˈʌvɚnɚ wˌʌt kˈaɪnd ʌv mˈɛn hiː hɐd kəmˈændᵻd æt ɡˈoːɹiː,
+DUMMY1/LJ050-0069.wav|ðə sˈiːkɹət sˈɜːvɪs hɐd ɹɪsˈiːvd fɹʌmðɪ ˌɛfbˌiːˈaɪ sˌʌm nˈaɪn θˈaʊzənd ɹɪpˈoːɹts ˌɑːn mˈɛmbɚz ʌvðə kˈɑːmjuːnˌɪst pˈɑːɹɾi.
+DUMMY1/LJ006-0202.wav|ðə nˈuːzvˈɛndɚ wʌz ˈɑːlsoʊ ɐ təbˈækənˌɪst,
+DUMMY1/LJ012-0230.wav|ʃˈɔːɹtli bɪfˌoːɹ ðə dˈeɪ fˈɪkst fɔːɹ ˌɛksɪkjˈuːʃən, bˈɪʃəp mˌeɪd ɐ fˈʊl kənfˈɛʃən, ðə bˈʌlk ʌvwˈɪtʃ bˈoːɹ ðɪ ɪmpɹˈɛs ʌv tɹˈuːθ,
+DUMMY1/LJ005-0248.wav|ænd stˈeɪɾᵻd ðæt ɪn hɪz əpˈɪniən nˈuːɡeɪt, æz ðə kˈɑːmən dʒˈeɪl ʌv mˈɪdəlsˌɛks, wʌz hˈoʊli ɪnˈædɪkwət tə ðə pɹˈɑːpɚ kənfˈaɪnmənt ʌv ɪts pɹˈɪzənɚz.
+DUMMY1/LJ037-0053.wav|hˌuː hɐdbɪn ɡɹˈeɪtli ʌpsˈɛt baɪ hɜːɹ ɛkspˈiəɹɪəns, wʌz ˈeɪbəl tə vjˈuː ɐ lˈaɪnʌp ʌv fˈoːɹ mˈɛn hˈændkʌft təɡˌɛðɚɹ æt ðə pəlˈiːs stˈeɪʃən.
+DUMMY1/LJ045-0177.wav|fɚðə fˈɜːst tˈaɪm
+DUMMY1/LJ004-0036.wav|ɪt wʌz hˈoʊpt ðæt ðɛɹ ɹˈuːlɚz wʊd hˈaɪɚɹ ɐkˌɑːmədˈeɪʃən ɪnðə kˈaʊnti pɹˈɪzənz, ænd ðætðɪ ɪnfˈiəɹɪɚɹ ɪstˈæblɪʃmənts wʊd ɪn kˈoːɹs ʌv tˈaɪm dˌɪsɐpˈɪɹ.
+DUMMY1/LJ026-0054.wav|kˌɑːɹboʊhˈaɪdɹeɪts stˈɑːɹtʃ, sˈɛljuːlˌoʊz ænd fˈæts.
+DUMMY1/LJ020-0085.wav|bɹˈeɪk ɐpˈɑːɹt fɹʌm wˈʌn ɐnˈʌðɚ ænd pˈaɪl ˌɑːn ɐ plˈeɪt, θɹˈoʊɪŋ ɐ klˈiːn dˈɔɪli ɔːɹ ɐ smˈɔːl nˈæpkɪn ˈoʊvɚ ðˌɛm. bɹˈeɪk ˈoʊpən æt tˈeɪbəl.
+DUMMY1/LJ046-0226.wav|ðə sˈɛvɹəl mˈɪlətˌɛɹi ɪntˈɛlɪdʒəns ˈeɪdʒənsiz ɹɪpˈoːɹɾᵻd kɹˈæŋk mˈeɪl ænd sˈɪmɪlɚ θɹˈɛts ɪnvˈɑːlvɪŋ ðə pɹˈɛzɪdənt.
+DUMMY1/LJ014-0233.wav|hiː ʃˈɑːt ɐn ˈoʊld sˈoʊldʒɚ hˌuː hɐd ɐtˈɛmptᵻd tə dɪtˈeɪn hˌɪm. hiː wʌz kənvˈɪktᵻd ænd ˈɛksɪkjˌuːɾᵻd.
+DUMMY1/LJ033-0152.wav|ðə pˈoːɹʃən ʌvðə pˈɑːm wˌɪtʃ wʌz aɪdˈɛntɪfˌaɪd wʌzðə hˈiːl ʌvðə ɹˈaɪt pˈɑːm, ˈaɪ.ˈiː., ðɪ ˈɛɹiə nˌɪɹ ðə ɹˈɪst, ɑːnðə lˈɪɾəl fˈɪŋɡɚ sˈaɪd.
+DUMMY1/LJ004-0009.wav|æz ˌɪndɪfˈæɾɪɡəbəl ænd sˈɛlfsˈækɹɪfˌaɪsɪŋ, fˈaʊnd baɪ pˈɜːsənəl vˌɪzɪtˈeɪʃən ðætðə kəndˈɪʃən ʌv dʒˈeɪlz θɹuːˈaʊt ðə kˈɪŋdəm wʌz,
+DUMMY1/LJ017-0134.wav|wɪðˌɪn ɐ fjˈuː wˈiːks əkˈɜːd ðə lˈiːdz pˈɔɪzənɪŋ kˈeɪs, ɪn wˌɪtʃ ðə mˈɜːdɚɹɚɹ ʌndˈaʊɾɪdli wʌz ɪnspˈaɪɚd baɪ ðə fˈækts mˌeɪd pˈʌblɪk æt pˈɑːmɚz tɹˈaɪəl.
+DUMMY1/LJ019-0318.wav|wʌz təbi ðə ɹˈuːl fɔːɹ ˈɔːl kənvˈɪktᵻd pɹˈɪzənɚz θɹuːˈaʊt ðɪ ˈɜːli stˈeɪdʒᵻz ʌv ðɛɹ dɪtˈɛnʃən;
+DUMMY1/LJ020-0093.wav|ɹˈaɪz, wˈɑːʃ fˈeɪs ænd hˈændz, ɹˈɪns ðə mˈaʊθ ˈaʊt ænd bɹˈʌʃ bˈæk ðə hˈɛɹ.
+DUMMY1/LJ012-0188.wav|pɹˈoʊbɚt wʌz ðˈɛn ɐdmˈɪɾᵻd æz ɐ wˈɪtnəs, ænd ðə kˈeɪs wʌz fˈʊli pɹˈuːvd ɐɡˈɛnst θˈɜːɾɛl, hˌuː wʌz hˈæŋd ɪn fɹˈʌnt ʌv hˈɜːtf��d dʒˈeɪl.
+DUMMY1/LJ019-0202.wav|ðə pɹˈɛfɹəns ɡˈɪvən tə ðə pˈɛntənvˌɪl sˈɪstəm dɪstɹˈɔɪd ˈɔːl hˈoʊps əvə kəmplˈiːt ɹɪfoːɹmˈeɪʃən ʌv nˈuːɡeɪt.
+DUMMY1/LJ039-0027.wav|ˈɑːswəldz ɹɪvˈɑːlvɚ
+DUMMY1/LJ040-0176.wav|hiː ɐdmˈɪɾᵻd tə fˈæntəsiz ɐbˌaʊt bˌiːɪŋ pˈaʊɚfəl ænd sˈʌmtaɪmz hˈɜːɾɪŋ ænd kˈɪlɪŋ pˈiːpəl, bˌʌt ɹɪfjˈuːzd tʊ ɪlˈæbɚɹˌeɪt ˈɑːn ðˌɛm.
+DUMMY1/LJ018-0354.wav|dˈaʊts wɜː lˈɑːŋ ˌɛntɚtˈeɪnd wˈɛðɚ tˈɑːməs wˈeɪnɹaɪt,
+DUMMY1/LJ031-0185.wav|fɹʌmðə pɹˌɛzɪdˈɛnʃəl ˈɛɹpleɪn, ðə vˈaɪs pɹˈɛzɪdənt tˈɛlɪfˌoʊnd ɐtˈɜːni dʒˈɛnɚɹəl ɹˈɑːbɚt ˈɛf. kˈɛnədi,
+DUMMY1/LJ006-0137.wav|ðeɪ wɜː nˌɑːt əblˈaɪdʒd tʊ ɐtˈɛnd tʃˈæpəl, ænd sˈɛldəm ɪf ˈɛvɚ wˈɛnt; "pɹˈɪzənɚz," sˈɛd wˈʌn ʌv ðˌɛm ˌʌndɚɹ ɛɡzˌæmᵻnˈeɪʃən, "dɪdnˌɑːt lˈaɪk ðə tɹˈʌbəl ʌv ɡˌoʊɪŋ tə tʃˈæpəl."
+DUMMY1/LJ032-0085.wav|ðə hˈaɪdəl sˈɪɡnɪtʃɚɹ ɑːnðə nˈoʊɾɪs ʌv klˌæsɪfɪkˈeɪʃən wʌz ɪnðə hˈændɹaɪɾɪŋ ʌv ˈɑːswəld.
+DUMMY1/LJ009-0037.wav|ðə skˈuːlmæstɚ ænd ðə dʒˈuːvənˌaɪl pɹˈɪzənɚz bˌiːɪŋ sˈiːɾᵻd ɹˈaʊnd ðə kəmjˈuːniəntˈeɪbəl, ˈɑːpəsˌɪt ðə pˈʌlpɪt.
+DUMMY1/LJ006-0021.wav|lˈeɪɾɚɹ ˌɑːn hiː hɐd dɪvˈoʊɾᵻd hɪmsˈɛlf tə ðə pˈɜːsənəl ɪnvˌɛstɪɡˈeɪʃən ʌvðə pɹˈɪzənz ʌvðə juːnˈaɪɾᵻd stˈeɪts.
+DUMMY1/LJ006-0082.wav|ænd ðɪs pɚtˈɪkjʊlɚɹ əfˈɪʃəl tˈʊk ˈɛksələnt kˈɛɹ tə sɪlˈɛkt æz ɹˈɛzɪdənts fɔːɹ hɪz ˈoʊn wˈɔːɹd ðoʊz mˈoʊst sˈuːɾəbəl fɹʌm hɪz ˈoʊn pˈɔɪnt ʌv vjˈuː.
+DUMMY1/LJ016-0380.wav|wɪð hˈoʊp tə ðə lˈæst. ðɛɹ ɪz ˈɔːlweɪz ðə tʃˈæns əvə flˈɔː ɪnðɪ ɪndˈaɪtmənt, əvə mˈɪsɪŋ wˈɪtnəs, ɔːɹ ɛkstˈɛnjuːˌeɪɾɪŋ sˈɜːkəmstˌænsᵻz.
+DUMMY1/LJ019-0344.wav|mˈɑːnɪɾɚ, ɔːɹ skˈuːlmæstɚ, nˈɔːɹ təbi ɛnɡˈeɪdʒd ɪnðə sˈɜːvɪs ʌv ˌɛni ˈɑːfɪsɚɹ ʌvðə pɹˈɪzən.
+DUMMY1/LJ019-0161.wav|ðiːz dˈɪsɪplˌɪnɛɹi ɪmpɹˈuːvmənts wɜː, haʊˈɛvɚ, ˈoʊnli slˈoʊli ænd ɡɹˈædʒuːəli ˌɪntɹədˈuːst.
+DUMMY1/LJ028-0145.wav|ænd hˈɪɹ ˈaɪ mˈeɪ nˌɑːt oʊmˈɪt tə tˈɛl ðə jˈuːs tʊ wˌɪtʃ ðə mˈoʊld dˈʌɡ ˌaʊɾəv ðə ɡɹˈeɪt mˈoʊt wʌz tˈɜːnd, nˈɔːɹ ðə mˈænɚ wˈɛɹɪn ðə wˈɔːl wʌz ɹˈɔːt.
+DUMMY1/LJ018-0349.wav|hɪz dɪsklˈeɪmɚ, dɪstˈɪŋkt ænd diːtˈeɪld ˌɑːn ˈɛvɹi pˈɔɪnt, wʌz ɪntˈɛndᵻd sˈɪmpli fɔːɹ ɪfˈɛkt.
+DUMMY1/LJ043-0010.wav|sˌʌm ʌvðə mˈɛmbɚz ʌv ðæt ɡɹˈuːp sˈɔː ɐ ɡˈʊd dˈiːl ʌvðɪ ˈɑːswəldz θɹuː ðə fˈɔːl ʌv naɪntˈiːn sˈɪkstiθɹˈiː,
+DUMMY1/LJ027-0178.wav|ðiːz wɜːɹ ʌndˈaʊɾɪdli pˈɛɹɪnˌɪbɹæntʃz. ɪnðə pˈɜːmiən ænd tɹaɪˈæsɪk hˈaɪɚ fˈɔːɹmz ɐpˈɪɹd, wˌɪtʃ wɜː sˈɜːtənli kˈædʒuːsˌɪbɹæntʃ.
+DUMMY1/LJ041-0070.wav|hiː dɪdnˌɑːt ɹˈaɪz əbˌʌv ðə ɹˈæŋk ʌv pɹˈaɪvət fˈɜːst klˈæs, ˈiːvən ðˌoʊ hiː hɐd pˈæst ɐ kwˈɑːlɪfˌaɪɪŋ ɛɡzˌæmᵻnˈeɪʃən fɚðə ɹˈæŋk ʌv kˈɔːɹpɹəl.
+DUMMY1/LJ008-0266.wav|ðˈʌs ɪnðə jˈɪɹz bɪtwˌiːn mˈeɪ fˈɜːst, eɪtˈiːn twˈɛntisˈɛvən, ænd θˈɜːɾiəθ ˈeɪpɹəl, eɪtˈiːn θˈɜːɾiwˌʌn,
+DUMMY1/LJ021-0091.wav|ɪn ðɪs ɹˈiːsənt ɹɪˌoːɹɡɐnaɪzˈeɪʃən wiː hæv ɹˈɛkəɡnˌaɪzd θɹˈiː dɪstˈɪŋkt fˈʌŋkʃənz:
+DUMMY1/LJ019-0129.wav|wˌɪtʃ mˈɑːɹkt ðə ɡɹˈoʊθ ʌv pˈʌblɪk ˈɪntɹəst ɪn pɹˈɪzən ɐfˈɛɹz, ænd wˌɪtʃ wʌzðə dʒˈɜːm ʌvðə nˈuː sˈɪstəm
+DUMMY1/LJ018-0215.wav|wˈɪljəm ɹˈuːpɛl wʌzðɪ ˈɛldəst bˌʌt ɪlədʒˈɪɾᵻmət sˈʌn əvə wˈɛlθi mˈæn hˌuː sˈʌbsɪkwəntli mˈæɹɪd ɹˈuːpɛlz mˈʌðɚ, ænd hɐd fˈɜːðɚ lədʒˈɪɾᵻmət ˈɪʃuː.
+DUMMY1/LJ015-0194.wav|ænd bɪhˈeɪvd sˌoʊ æz tə dʒˈʌstɪfˌaɪ ɐ bɪlˈiːf ðæt hiː hɐdbɪn ɐ dʒˈeɪlbˈɜːd ˈɔːl hɪz lˈaɪf.
+DUMMY1/LJ016-0137.wav|ðæt nˈʌmbɚz ʌv mˈɛn, "lˈaɪfɚz," ænd ˈʌðɚz wɪð tˈɛn, foːɹtˈiːn, ɔːɹ twˈɛnti jˈɪɹz tə dˈuː, kæn biː tɹˈʌstᵻd tə wˈɜːk ˌaʊɾəv dˈoːɹz wɪðˌaʊt bˈoʊlts ænd bˈɑːɹz
+DUMMY1/LJ002-0289.wav|ðə lˈæɾɚ ɹˈeɪzd eɪtˈiːn pˈɛns ɐmˌʌŋ ðˌɛm tə pˈeɪ fɚɹə tɹˈʌs ʌv stɹˈɔː fɚðə pˈʊɹ wˈʊmən tə lˈaɪ ˈɑːn.
+DUMMY1/LJ023-0016.wav|ɪn naɪntˈiːn θˈɜːɾiθɹˈiː juː ænd ˈaɪ nˈuː ðæt wiː mˈʌst nˈɛvɚ lˈɛt ˌaʊɚɹ ˌiːkənˈɑːmɪk sˈɪstəm ɡɛt kəmplˈiːtli ˌaʊɾəv dʒˈɔɪnt ɐɡˈɛn
+DUMMY1/LJ011-0141.wav|ðɛɹwˌɜːɹ æt ðə mˈoʊmənt ɪn nˈuːɡeɪt sˈɪks kˈɑːnvɪkts sˈɛntənst tə dˈɛθ fɔːɹ fˈɔːɹdʒɪŋ wˈɪlz.
+DUMMY1/LJ016-0283.wav|tə dˈuː ðˌɛm mˈɪɹ dʒˈʌstɪs, ðɛɹwˌʌz æt lˈiːst tˈɪl ðˈɛn �� hˈæfdɹˈʌŋkən ɹˈɪbɔːld ɡˈeɪəɾi ɐmˌʌŋ ðə kɹˈaʊd ðæt mˌeɪd ðˌɛm ˈɔːl ɐkˈɪn."
+DUMMY1/LJ035-0082.wav|ðɪ ˈoʊnli ˈɪntɚvəl wʌzðə tˈaɪm nˈɛsəsɚɹi tə ɹˈaɪd ɪnðɪ ˈɛlɪvˌeɪɾɚ fɹʌmðə sˈɛkənd tə ðə sˈɪksθ flˈoːɹ ænd wˈɔːk bˈæk tə ðə sˈaʊθiːst kˈɔːɹnɚ.
+DUMMY1/LJ045-0194.wav|ˈɛnɪwˌʌn hˌuː wʌz fəmˈɪlɪɹ wɪð ðæt ˈɛɹiə ʌv dˈæləs wʊdhɐv nˈoʊn ðætðə mˈoʊɾɚkˌeɪd wʊd pɹˈɑːbəbli pˈæs ðə tˈɛksəs skˈuːl bˈʊk dɪpˈɑːsɪtˌoːɹi tə ɡɛt fɹʌm mˈeɪn stɹˈiːt
+DUMMY1/LJ009-0124.wav|ˈɑːkjʊpˌaɪd wɛn ðeɪ sˈɔː ɪt lˈæst, bˌʌt ɐ fjˈuː ˈaɪʊɹz ɐɡˈoʊ, baɪ ðɛɹ kˈɑːmɹædz hˌuː ɑːɹ nˈaʊ dˈɛd;
+DUMMY1/LJ030-0162.wav|ɪnðə pɹˌɛzɪdˈɛnʃəl lˈɪməzˌiːn
+DUMMY1/LJ050-0223.wav|ðə plˈæn pɹəvˈaɪdz fɚɹən ɐdˈɪʃənəl tˈuː hˈʌndɹəd fˈaɪv ˈeɪdʒənts fɚðə sˈiːkɹət sˈɜːvɪs. sˌɛvəntˈiːn ʌv ðɪs nˈʌmbɚɹ ɑːɹ pɹəpˈoʊzd fɚðə pɹətˈɛktɪv ɹɪsˈɜːtʃ sˈɛkʃən;
+DUMMY1/LJ008-0228.wav|ðɛɹ hˈɑːɹʃ ænd hˈæfkɹˈækt vˈɔɪsᵻz fˈʊl ʌv mˈɔːdlɪn, bɪsˈɑːɾᵻd sˈɪmpəθi fɔːɹ ðoʊz ɐbˌaʊt tə dˈaɪ.
+DUMMY1/LJ002-0096.wav|ðɪ ˈeɪt kˈoːɹts əbˌʌv ɪnˈuːmɚɹˌeɪɾᵻd wɜː wˈɛl səplˈaɪd wɪð wˈɔːɾɚ;
+DUMMY1/LJ018-0288.wav|ˈæftɚ ðɪs ðɪ ˈʌðɚ kənspˈɪɹəɾɚz tɹˈævəld tʊ əbtˈeɪn dʒˈɛnjuːᵻn bˈɪlz ænd mˈæstɚ ðə sˈɪstəm ʌvðə lˈiːdɪŋ hˈaʊzɪz æt hˈoʊm ænd ɐbɹˈɔːd.
+DUMMY1/LJ002-0106.wav|ɪn wˌɪtʃ lˈæɾɚli ɐ kˈɑːpɚ hɐdbɪn fˈɪkst fɚðə kˈʊkɪŋ ʌv pɹəvˈɪʒənz sˈɛnt ɪn baɪ tʃˈæɹɪɾəbəl pˈɜːsənz.
+DUMMY1/LJ025-0129.wav|ˌɑːn ˈiːtʃ lˈoʊb ʌvðə bˈaɪlˈoʊbd lˈiːf ʌv vˈiːnəs flˈaɪtɹæp ɑːɹ θɹˈiː dˈɛlᵻkət fˈɪləmənts wˌɪtʃ stˈænd ˈaʊt æt ɹˈaɪt ˈæŋɡəlz fɹʌmðə sˈɜːfɪs ʌvðə lˈiːf.
+DUMMY1/LJ044-0013.wav|hˈændz ˈɔf kjˈuːbə, ˈɛnd kwˈoʊt, ɐn ˌæplɪkˈeɪʃən fˈɔːɹm fɔːɹ, ænd ɐ mˈɛmbɚʃˌɪp kˈɑːɹd ˈɪn,
+DUMMY1/LJ049-0115.wav|ʌvðə pˈɜːsən hˌuː ɪz ˈæktʃuːəli ɪnðɪ ˈɛksɚsˌaɪz ʌvðɪ ɛɡzˈɛkjuːtˌɪv pˈaʊɚ, ˈɔːɹ
+DUMMY1/LJ019-0145.wav|bˌʌt ɹɪfoːɹmˈeɪʃən wʌz ˈoʊnli skˈɪn dˈiːp. bɪlˌoʊ ðə sˈɜːfɪs mˈɛnɪəv ðɪ ˈoʊld ˈiːvəlz stˈɪl ɹˈæŋkəld.
+DUMMY1/LJ019-0355.wav|kˈeɪm ˌʌp ɪn ˈɔːl ɹɪspˈɛkts tə mˈɑːdɚn ɹɪkwˈaɪɚmənts.
+DUMMY1/LJ019-0289.wav|ðɛɹwˌʌz ˌʌnɹɪstɹˈeɪnd ɐsˈoʊsɪˈeɪʃən ʌv ʌntɹˈaɪd ænd kənvˈɪktᵻd, dʒˈuːvənˌaɪl wɪð ɐdˈʌlt pɹˈɪzənɚz, vˈeɪɡɹənts, mˌɪsdɪmˈiːnənts, fˈɛlənz.
+DUMMY1/LJ048-0222.wav|ɪn fˈɔːɹt wˈɜːθ, ðɛɹ əkˈɜːd ɐ bɹˈiːtʃ ʌv dˈɪsɪplˌɪn baɪ sˌʌm mˈɛmbɚz ʌvðə sˈiːkɹət sˈɜːvɪs hˌuː wɜːɹ əfˈɪʃəli tɹˈævəlɪŋ wɪððə pɹˈɛzɪdənt.
+DUMMY1/LJ016-0367.wav|ˌʌndɚ ðə nˈuː sˈɪstəm ðə hˈoʊl ʌvðɪ ɐɹˈeɪndʒmənts fɹʌm fˈɜːst tə lˈæst fˈɛl əpˌɑːn ðɪ ˈɑːfɪsɚz.
+DUMMY1/LJ047-0097.wav|ˈeɪdʒənt kwˈɪɡli dɪdnˌɑːt nˈoʊ ʌv ˈɑːswəldz pɹˈaɪɚɹ ˌɛfbˌiːˈaɪ ɹˈɛkɚd wɛn hiː ˈɪntɚvjˌuːd hˌɪm,
+DUMMY1/LJ007-0075.wav|æz ɪfˈɛktʃuːəli tə ɹɪbjˈuːk ænd ɐbˈæʃ ðə pɹəfˈeɪn spˈɪɹɪt ʌvðə mˈoːɹ ˈɪnsələnt ænd dˈɛɹɹɪŋ ʌvðə kɹˈɪmɪnəlz.
+DUMMY1/LJ047-0022.wav|pɹəvˈaɪdᵻd baɪ ˈʌðɚɹ ˈeɪdʒənsiz.
+DUMMY1/LJ007-0085.wav|æt nˈuːɡeɪt ænd jˈɔːɹk kˈæsəl æz lˈɑːŋ æz fˈaɪv jˈɪɹz; "æt ˈɪltʃɛstɚ ænd mˈɔːɹpəθ fɔːɹ sˈɛvən jˈɪɹz; æt wˈɔːɹɪk fɔːɹ ˈeɪt jˈɪɹz,
+DUMMY1/LJ047-0075.wav|hˈɑːsti hɐd ɪnkwˈaɪɚd ˈɜːlɪɚ ænd fˈaʊnd nˈoʊ ˈɛvɪdəns ðˌɐɾɪt wʌz fˈʌŋkʃənɪŋ ɪnðə dˈæləs ˈɛɹiə.
+DUMMY1/LJ008-0098.wav|wˈʌn wʌzðə "jˈoʊmən ʌvðə hˈɑːltɚ," ɐ nˈuːɡeɪt əfˈɪʃəl, ðɪ ˌɛksɪkjˈuːʃənɚz ɐsˈɪstənt, hˈuːm mˈɪstɚ dʒˈeɪ. tˈiː. smˈɪθ, hˌuː wʌz pɹˈɛzənt æt ðɪ ˌɛksɪkjˈuːʃən,
+DUMMY1/LJ017-0102.wav|ðə sˈɛkənd ɐtˈæk wʌz fˈeɪɾəl, ænd ˈɛndᵻd ɪn kˈʊks dˈɛθ fɹʌm tˈɛtənəs.
+DUMMY1/LJ046-0105.wav|sˈɛkənd, ðɪ ˈædɪkwəsi ʌv ˈʌðɚɹ ɐdvˈæns pɹˌɛpɚɹˈeɪʃənz fɚðə sɪkjˈʊɹɪɾi ʌvðə pɹˈɛzɪdənt, dˈʊɹɪŋ hɪz vˈɪzɪt tə dˈæləs,
+DUMMY1/LJ018-0206.wav|hiː wʌzɐ tˈɔːl, slˈɛndɚ mˈæn, wɪð ɐ lˈɑːŋ fˈeɪs ænd ˈaɪɚnɡɹˈeɪ hˈɛɹ.
+DUMMY1/LJ012-0271.wav|wˈɛðɚɹ ɪt wʌz ɡɹˈiːd ɔːɹ ɐ kwˈɔːɹəl ðæt dɹˈoʊv ɡɹˈiːnækɚ tə ðə dˈɛspɚɹət dˈiːd ɹɪmˈeɪnz əbskjˈʊɹ.
+DUMMY1/LJ005-0086.wav|wɪð sˈʌtʃ fˈɜːðɚ sˌɛpɚɹˈeɪʃən æz ðə dʒˈʌstɪsᵻz ʃˌʊd dˈiːm kəndˈuːsɪv tə ɡˈʊd ˈɔːɹdɚ ænd dˈɪsɪplˌɪn.
+DUMMY1/LJ042-0097.wav|ænd kənsˈɪdɚɹəbli bˈɛɾɚ lˈɪvɪŋ kwˈɔːɹ��ɚz ðɐn ðoʊz ɐkˈoːɹdᵻd tə sˈoʊviət sˈɪɾɪzənz ʌv ˈiːkwəl ˈeɪdʒ ænd stˈeɪʃən.
+DUMMY1/LJ047-0126.wav|wiː wʊd hˈændəl ɪt ɪn dˈuː kˈoːɹs, ɪn ɐkˈoːɹd wɪððə hˈoʊl kˈɑːntɛkst ʌvðɪ ɪnvˌɛstɪɡˈeɪʃən. ˈɛnd kwˈoʊt.
+DUMMY1/LJ041-0022.wav|ˈɑːswəld fˈɜːst ɹˈoʊt, kwˈoʊt, ˈɛdwɚd vˈoʊdʒəl, ˈɛnd kwˈoʊt, ɐn ˈɑːbvɪəs mɪsspˈɛlɪŋ ʌv vˈoʊbəlz nˈeɪm,
+DUMMY1/LJ015-0025.wav|ðə bˈæŋk ɛndʒˈɔɪd ɐn ˈɛksələnt ɹˌɛpjuːtˈeɪʃən, ɪt hɐd ɐ ɡˈʊd kənˈɛkʃən, ænd wʌz sʌpˈoʊzd təbi pˈɜːfɛktli sˈaʊnd.
+DUMMY1/LJ012-0194.wav|bˌʌt bˈɜːk ænd hˈɛɹ hɐd ðɛɹ ˈɪmᵻtˌeɪɾɚz fˈɜːðɚ sˈaʊθ,
+DUMMY1/LJ028-0416.wav| ɪf mˈæn mˈeɪ spˈiːk sˌoʊ kˈɑːnfɪdəntli ʌv hɪz ɡɹˈeɪt ɪmpˈɛnɪtɹəbəl kˈaʊnsəlz, fɚɹən ɪtˈɜːnəl tˈɛstᵻməni ʌv hɪz ɡɹˈeɪt wˈɜːk ɪnðə kənfjˈuːʒən ʌv mˈænz pɹˈaɪd,
+DUMMY1/LJ007-0130.wav|ɑːɹ ˈɔːl hˈʌdəld təɡˌɛðɚ wɪðˌaʊt dɪskɹˌɪmᵻnˈeɪʃən, ˈoʊvɚsˌaɪt, ɔːɹ kəntɹˈoʊl."
+DUMMY1/LJ015-0005.wav|ɐbˌaʊt ðɪs tˈaɪm dˈeɪvɪdsən ænd ɡˈoːɹdən, ðə pˈiːpəl əbˌʌvmˈɛnʃənd,
+DUMMY1/LJ016-0125.wav|wɪð ðˈɪs, plˈeɪst ɐɡˈɛnst ðə wˈɔːl nˌɪɹ ðə ʃˈɛvɔːksdəfɹˈaɪz, hiː mˌeɪd ɐn ˈɛskɐlˌeɪd.
+DUMMY1/LJ014-0224.wav|æz dwˈaɪɚ sɚvˈaɪvd, kˈænən ɛskˈeɪpt ðə dˈɛθ sˈɛntəns, wˌɪtʃ wʌz kəmjˈuːɾᵻd tə pˈiːnəl sˈɜːvɪtˌuːd fɔːɹ lˈaɪf.
+DUMMY1/LJ005-0019.wav|ɹɪfjˈuːɾᵻd baɪ ɐbˈʌndənt ˈɛvɪdəns, ænd hˌævɪŋ nˈoʊ faʊndˈeɪʃən wʌtˈɛvɚɹ ɪn tɹˈuːθ.
+DUMMY1/LJ042-0221.wav|wɪð ˈiːðɚ ɡɹˈeɪt æmbˈɪvələns, ɔːɹ kˈoʊld kˌælkjʊlˈeɪʃən hiː pɹɪpˈɛɹd kəmplˈiːtli dˈɪfɹənt ˈænsɚz tə ðə sˈeɪm kwˈɛstʃənz.
+DUMMY1/LJ001-0063.wav|wˌɪtʃ wʌz dʒˈɛnɚɹəli mˈoːɹ fˈɔːɹməli ɡˈɑːθɪk ðɐn ðə pɹˈɪntɪŋ ʌvðə dʒˈɜːmən wˈɜːkmɛn,
+DUMMY1/LJ030-0006.wav|ðeɪ tˈʊk ˈɔf ɪnðə pɹˌɛzɪdˈɛnʃəl plˈeɪn, ˈɛɹ fˈoːɹs wˌʌn, æt ɪlˈɛvən ˈeɪ.ˈɛm., ɐɹˈaɪvɪŋ æt sˌæn æntˈoʊnɪˌoʊ æt wˌʌn:θˈɜːɾi pˈiː.ˈɛm., ˈiːstɚn stˈændɚd tˈaɪm.
+DUMMY1/LJ024-0054.wav|dɪmˈɑːkɹəsi wɪl hæv fˈeɪld fˈɑːɹ bɪjˌɑːnd ðɪ ɪmpˈoːɹtəns tʊ ɪt ʌv ˌɛni kˈɪŋ ʌv pɹˈɛsɪdənt kənsˈɜːnɪŋ ðə dʒuːdˈɪʃɚɹi.
+DUMMY1/LJ006-0044.wav|ðə sˈeɪm kˈæləs ɪndˈɪfɹəns tə ðə mˈɔːɹəl wˈɛlbˌiːɪŋ ʌvðə pɹˈɪzənɚz, ðə sˈeɪm wˈɑːnt ʌv ɛmplˈɔɪmənt ænd ʌv ˈɔːl dˈɪsɪplˌɪnɛɹi kəntɹˈoʊl.
+DUMMY1/LJ039-0154.wav|fˈoːɹ pˈɔɪnt ˈeɪt tə fˈaɪv pˈɔɪnt sˈɪks sˈɛkəndz ɪf ðə sˈɛkənd ʃˈɑːt mˈɪst,
+DUMMY1/LJ050-0090.wav|ðeɪ sˈiːm ʌndjˈuːli ɹɪstɹˈɪktɪv ɪn kəntˈɪnjuːɪŋ tə ɹɪkwˈaɪɚ sˌʌm mˌænɪfɪstˈeɪʃən ʌv ˈænɪməs ɐɡˈɛnst ɐ ɡˈʌvɚnmənt əfˈɪʃəl.
+DUMMY1/LJ028-0421.wav|ɪt wʌzðə bɪɡˈɪnɪŋ ʌvðə ɡɹˈeɪt kəlˈɛkʃənz ʌv bˌæbɪlˈoʊniən æntˈɪkwɪɾiz ɪnðə mjuːzˈiəmz ʌvðə wˈɛstɚn wˈɜːld.
+DUMMY1/LJ033-0205.wav|ðˈɛn ˈaɪ wʊd sˈeɪ ðə pˌɑːsəbˈɪlɪɾi ɛɡzˈɪsts, ðiːz fˈaɪbɚz kˌʊdɐv kˈʌm fɹʌm ðɪs blˈæŋkɪt, ˈɛnd kwˈoʊt.
+DUMMY1/LJ019-0335.wav|ðə bˈʊks ænd dʒˈɜːnəlz hiː wʌz tə kˈiːp wɜː maɪnjˈuːtli spˈɛsɪfˌaɪd, ænd hɪz kˈɑːnstənt pɹˈɛzəns ɪn ɔːɹ nˌɪɹ ðə dʒˈeɪl wʌz ɪnsˈɪstᵻd əpˌɑːn.
+DUMMY1/LJ013-0045.wav|wˈɑːlᵻsᵻz ɹɪlˈeɪʃənz wˈɔːɹnd hˌɪm ɐɡˈɛnst hɪz lˈɪvɚpˌuːl fɹˈɛnd,
+DUMMY1/LJ037-0002.wav|tʃˈæptɚ fˈoːɹ. ðɪ ɐsˈæsɪn: pˈɑːɹt sˈɪks.
+DUMMY1/LJ018-0159.wav|ðɪs wʌz ˈɔːl ðə pəlˈiːs wˈɑːntᵻd tə nˈoʊ.
+DUMMY1/LJ026-0140.wav|ɪnðə plˈænt æz ɪnðɪ ˈænɪməl mətˈæbəlˌɪzəm mˈʌst kənsˈɪst ʌv ˌænɐbˈɑːlɪk ænd kˌæɾɐbˈɑːlɪk pɹˈɑːsɛsᵻz.
+DUMMY1/LJ014-0171.wav|ˈaɪ wɪl bɹˈiːfli dɪskɹˈaɪb wˈʌn ɔːɹ tˈuː ʌvðə mˈoːɹ ɹɪmˈɑːɹkəbəl mˈɜːdɚz ɪnðə jˈɪɹz ɪmˈiːdɪətli fˈɑːloʊɪŋ, ðˈɛn pˈæs ˌɑːn tʊ ɐnˈʌðɚ bɹˈæntʃ ʌv kɹˈaɪm.
+DUMMY1/LJ037-0007.wav|θɹˈiː ˈʌðɚz sˈʌbsɪkwəntli aɪdˈɛntɪfˌaɪd ˈɑːswəld fɹʌm ɐ fˈoʊɾəɡɹˌæf.
+DUMMY1/LJ033-0174.wav|mˌaɪkɹəskˈɑːpɪk ænd jˌuːvˈiː ˈʌltɹə vˈaɪələt kˌæɹɪktɚɹˈɪstɪks, ˈɛnd kwˈoʊt.
+DUMMY1/LJ040-0110.wav|hiː ɐpˈæɹəntli ɐdʒˈʌstᵻd wˈɛl ɪnˈʌf ðɛɹ tə hæv hɐd ɐn ˈævɹɪdʒ, ɑːlðˈoʊ ɡɹˈædʒuːəli dɪtˈiəɹɪɹˌeɪɾɪŋ, skˈuːl ɹˈɛkɚd
+DUMMY1/LJ039-0192.wav|hiː hɐd ɐ tˈoʊɾəl ʌv bɪtwˌiːn fˈoːɹ pˈɔɪnt ˈeɪt ænd fˈaɪv pˈɔɪnt sˈɪks sˈɛkəndz bɪtwˌiːn ðə tˈuː ʃˈɑːts wˌɪtʃ hˈɪt
+DUMMY1/LJ032-0261.wav|wˌɛn hiː ɐpˈɪɹd bɪfˌoːɹ ðə kəmˈɪʃən, mˈaɪkəl pˈeɪn lˈɪftᵻd ðə blˈæŋkɪt
+DUMMY1/LJ040-0097.wav|lˈiː wʌz bɹˈɔːt ˌʌp ɪn ðɪs ˈætməsfˌɪɹ ʌv kˈɑːnstənt mˈʌni pɹˈɑːbləmz, ænd ˈaɪ æm ʃˈʊɹ ɪt hɐd kwˈaɪt ɐn ɪfˈɛkt ˈɑːn hˌɪm, ænd ˈɑːlsoʊ ɹˈɑːbɚt, ˈɛnd kwˈoʊt.
+DUMMY1/LJ037-0249.wav|mɪsˈɛs ˈɜːliːn ɹˈɑːbɚts, ðə hˈaʊskiːpɚɹ æt ˈɑːswəldz ɹˈuːmɪŋhˌaʊs ænd ðə lˈæst pˈɜːsən nˈoʊn tə hæv sˈiːn hˌɪm bɪfˌoːɹ hiː ɹˈiːtʃt tˈɛnθ stɹˈiːt ænd pˈæʔn̩ ˈævənˌuː,
+DUMMY1/LJ016-0248.wav|mˈɑːɹwʊd wʌz pɹˈaʊd ʌv hɪz kˈɔːlɪŋ, ænd wɛn kwˈɛstʃənd æz tə wˈɛðɚ hɪz pɹˈɑːsɛs wʌz sˌæɾɪsfˈæktɚɹi, ɹɪplˈaɪd ðæt hiː hˈɜːd "nˈoʊ kəmplˈeɪnts."
+DUMMY1/LJ004-0083.wav|æz mˈɪstɚ bˈʌkstən pˈɔɪntᵻd ˈaʊt, mˈɛni ˈoʊld ˈækts ʌv pˈɑːɹləmənt dɪzˈaɪnd tə pɹətˈɛkt ðə pɹˈɪzənɚ wɜː stˈɪl ɪn fˈʊl fˈoːɹs.
+DUMMY1/LJ014-0029.wav|ðɪs wʌz dɪlˈɑːɹuːz wˈɑːtʃ, fˈʊli aɪdˈɛntɪfˌaɪd æz sˈʌtʃ, wˌɪtʃ hˈɑːkɚ tˈoʊld hɪz bɹˈʌðɚ dɪlˈɑːɹuː hɐd ɡˈɪvən hˌɪm ðə mˈɔːɹnɪŋ ʌvðə mˈɜːdɚ.
+DUMMY1/LJ021-0110.wav|hɐvbɪn bˈɛst kˈælkjʊlˌeɪɾᵻd tə pɹəmˈoʊt ɪndˈʌstɹɪəl ɹɪkˈʌvɚɹi ænd ɐ pˈɜːmənənt ɪmpɹˈuːvmənt ʌv bˈɪznəs ænd lˈeɪbɚ kəndˈɪʃənz.
+DUMMY1/LJ003-0107.wav|hiː slˈɛpt ɪnðə sˈeɪm bˈɛd wɪð ɐ hˈaɪweɪmən ˌɑːn wˈʌn sˈaɪd, ænd ɐ mˈæn tʃˈɑːɹdʒd wɪð mˈɜːdɚɹ ɑːnðɪ ˈʌðɚ.
+DUMMY1/LJ039-0076.wav|ɹˈɑːnəld sˈɪmənz, tʃˈiːf ʌvðə jˈuː.ˈɛs. ˈɑːɹmi ˈɪnfəntɹi wˈɛpənz ɪvˌæljuːˈeɪʃən bɹˈæntʃ ʌvðə bɐlˈɪstɪks ɹɪsˈɜːtʃ lˈæbɹətˌɔːɹi, sˈɛd, kwˈoʊt,
+DUMMY1/LJ016-0347.wav|hɐd ʌndˈaʊɾɪdli ɐ sˈɑːləm, ɪmpɹˈɛsɪv ɪfˈɛkt əpˌɑːn ðoʊz aʊtsˈaɪd.
+DUMMY1/LJ001-0072.wav|ˈæftɚ ðɪ ˈɛnd ʌvðə fˈɪftiːnθ sˈɛntʃɚɹi ðə dɪɡɹɐdˈeɪʃən ʌv pɹˈɪntɪŋ, ɪspˈɛʃəli ɪn dʒˈɜːməni ænd ˈɪɾəli,
+DUMMY1/LJ024-0018.wav|kˈɑːnsɪkwəntli, ɑːlðˈoʊ ðɛɹ nˈɛvɚ kæn biː mˈoːɹ ðɐn fɪftˈiːn, ðɛɹ mˈeɪ biː ˈoʊnli foːɹtˈiːn, ɔːɹ θɜːtˈiːn, ɔːɹ twˈɛlv.
+DUMMY1/LJ032-0180.wav|ðætðə fˈaɪbɚz wɜː kˈɔːt ɪnðə kɹˈɛvɪs ʌvðə ɹˈaɪfəlz bˈʌt plˈeɪt, kwˈoʊt, ɪnðə ɹˈiːsənt pˈæst, ˈɛnd kwˈoʊt,
+DUMMY1/LJ010-0083.wav|ænd mˈɛʒɚz tˈeɪkən tʊ ɐɹˈɛst ðˌɛm wɛn ðɛɹ plˈænz wɜː sˈoʊ fˌɑːɹ dɪvˈɛləpt ðæt nˈoʊ dˈaʊt kʊd ɹɪmˈeɪn æz tə ðɛɹ ɡˈɪlt.
+DUMMY1/LJ002-0299.wav|ænd ɡˈeɪv ðə ɡˈɑːɹnɪʃ fɚðə kˈɑːmən sˈaɪd æt ðæt sˈʌm, wˌɪtʃ ɪz fˈaɪv ʃˈɪlɪŋz mˈoːɹ ðɐn mˈɪstɚ nˈiːld sˈɛz wʌz ɛkstˈɔːɹɾᵻd ɑːnðə kˈɑːmən sˈaɪd.
+DUMMY1/LJ048-0143.wav|ðə sˈiːkɹət sˈɜːvɪs dɪdnˌɑːt æt ðə tˈaɪm ʌvðɪ ɐsˌæsᵻnˈeɪʃən hæv ˌɛni ɪstˈæblɪʃt pɹəsˈiːdʒɚ ɡˈʌvɚnɪŋ ɪts ɹɪlˈeɪʃənʃˌɪps wɪð ðˌɛm.
+DUMMY1/LJ012-0054.wav|sˈɑːlɑːmənz, wˌaɪl wˈeɪɾɪŋ tʊ ɐpˈɪɹ ɪn kˈoːɹt, pɚswˈeɪdᵻd ðə tˈɜːnkiːz tə tˈeɪk hˌɪm tʊ ɐ pˈʌblɪkhˈaʊs, wˌɛɹ ˈɔːl mˈaɪt "ɹɪfɹˈɛʃ."
+DUMMY1/LJ019-0270.wav|vˈɛdʒɪɾəbəlz, ɪspˈɛʃəli ðə pətˈeɪɾoʊ, ðæt mˈoʊst vˈæljuːəbəl ˈæntaɪskoːɹbjˈuːɾɪk, wʌz tˈuː ˈɔfən oʊmˈɪɾᵻd.
+DUMMY1/LJ035-0164.wav|θɹˈiː mˈɪnɪts ˈæftɚ ðə ʃˈuːɾɪŋ.
+DUMMY1/LJ014-0326.wav|mˈæltbi ænd kˈʌmpəni wʊd ˈɪʃuː wˈɔːɹənts ˌɑːn ðˌɛm dɪlˈɪvɚɹəbəl tə ðɪ ɪmpˈoːɹɾɚ, ænd ðə ɡˈʊdz wɜː ðˈɛn pˈæst təbi stˈoːɹd ɪn nˈeɪbɚɹɪŋ wˈɛɹhaʊzɪz.
+DUMMY1/LJ001-0173.wav|ðɪ ɪsˈɛnʃəl pˈɔɪnt təbi ɹɪmˈɛmbɚd ɪz ðætðɪ ˈɔːɹnəmənt, wʌtˈɛvɚɹ ɪt ˈɪz, wˈɛðɚ pˈɪktʃɚ ɔːɹ pˈætɚnwˈɜːk, ʃˌʊd fˈɔːɹm pˈɑːɹt ʌvðə pˈeɪdʒ,
+DUMMY1/LJ050-0056.wav|ˌɑːn dᵻsˈɛmbɚ twˈɛntisˈɪks, naɪntˈiːn sˈɪkstiθɹˈiː, ðɪ ˌɛfbˌiːˈaɪ sˈɜːkjʊlˌeɪɾᵻd ɐdˈɪʃənəl ɪnstɹˈʌkʃənz tʊ ˈɔːl ɪts ˈeɪdʒənts,
+DUMMY1/LJ003-0319.wav|pɹəvˈaɪdᵻd ˈoʊnli ðæt ðɛɹ sɪkjˈʊɹɪɾi wʌz nˌɑːt dʒˈɛpɚdˌaɪzd, ænd dɪpˈɛndənt əpˌɑːn ðɪ ɛnfˈoːɹsmənt ʌv ɐnˈʌðɚ nˈuː ɹˈuːl,
+DUMMY1/LJ006-0040.wav|ðə fˈækt wʌz ðætðə jˈɪɹz æz ðeɪ pˈæst, nˌɪɹli twˈɛnti ɪn ˈɔːl, hɐd wˈɜːkt bˌʌt lˈɪɾəl pˈɜːmənənt ɪmpɹˈuːvmənt ɪn ðɪs dɪtˈɛstəbəl pɹˈɪzən.
+DUMMY1/LJ017-0231.wav|hɪz bˈɑːdi wʌz fˈaʊnd lˈaɪɪŋ ɪn ɐ pˈuːl ʌv blˈʌd ɪn ɐ nˈaɪtdɹˈɛs, stˈæbd ˌoʊvɚ ænd ˌoʊvɚɹ ɐɡˈɛn ɪnðə lˈɛft sˈaɪd.
+DUMMY1/LJ017-0226.wav|wˈʌn hˈæf ʌvðə mjˌuːtɪnˈɪɹz fˈɛl əpˌɑːn hˌɪm ˌʌnəwˈɛɹz wɪð hˈændspaɪks ænd kˈæpstənbˈɑːɹz.
+DUMMY1/LJ004-0239.wav|hiː hɐdbɪn kəmˈɪɾᵻd fɚɹən əfˈɛns fɔːɹ wˌɪtʃ hiː wʌz ɐkwˈɪɾᵻd.
+DUMMY1/LJ048-0112.wav|ðə kəmˈɪʃən ˈɑːlsoʊ ɹɪɡˈɑːɹdz ðə sɪkjˈʊɹɪɾi ɐɹˈeɪndʒmənts wˈɜːkt ˈaʊt baɪ lˈɔːsən ænd sˈɔːɹəlz æt lˈʌv fˈiːld æz ɛntˈaɪɚli ˈædɪkwət.
+DUMMY1/LJ039-0125.wav|ðæt ˈɑːswəld wʌzɐ ɡˈʊd ʃˈɑːt, sˈʌmwʌt bˈɛɾɚ ðɐn ɔːɹ ˈiːkwəl tʊ bˈɛɾɚ ðɐn ðɪ ˈævɹɪdʒ lˈɛt ˌʌs sˈeɪ.
+DUMMY1/LJ030-0196.wav|hiː kɹˈaɪd ˈaʊt, kwˈoʊt, ˈoʊ, nˈoʊ, nˈoʊ, nˈoʊ. maɪ ɡˈɑːd, ðeɪ ɑːɹ ɡˌoʊɪŋ tə kˈɪl ˌʌs ˈɔːl, ˈɛnd kwˈoʊt,
+DUMMY1/LJ010-0228.wav|hiː wʌz ɹɪlˈiːsd fɹʌm bɹˈɔːdmoːɹ ɪn eɪtˈiːn sˈɛvəntiˈeɪt, ænd wɛnt ɐbɹˈɔːd.
+DUMMY1/LJ045-0228.wav|ɑːnðɪ ˈʌðɚ hˈænd, hiː kˌʊdɐv tɹˈævəld sˌʌm dˈɪstəns wɪððə mˈʌni hiː dˈɪd hæv ænd hiː dˈɪd ɹɪtˈɜːn tə hɪz ɹˈuːm wˌɛɹ hiː əbtˈeɪnd hɪz ɹɪvˈɑːlvɚ.
+DUMMY1/LJ028-0168.wav|ɪnðɪ ˈʌðɚ wʌzðə sˈeɪkɹəd pɹˈiːsɪŋkt ʌv dʒˈuːpɪɾɚ bɪlˈuːz,
+DUMMY1/LJ021-0140.wav|ænd ɪn sˈʌtʃ ɐn ˈɛfɚt wiː ʃˌʊd biː ˈeɪbəl tə sɪkjˈʊɹ fɔːɹ ɛmplˈɔɪɚz ænd ɛmplˈɔɪiːz ænd kənsˈuːmɚz
+DUMMY1/LJ009-0280.wav|ɐɡˈɛn ðə ɹˈɛtʃᵻd kɹˈiːtʃɚ səksˈiːdᵻd ɪn əbtˈeɪnɪŋ fˈʊthoʊld, bˌʌt ðɪs tˈaɪm ɑːnðə lˈɛft sˈaɪd ʌvðə dɹˈɑːp.
+DUMMY1/LJ003-0159.wav|tə kˈɑːnstɪtˌuːt ðɪs ðɪ ɐɹˌɪstəkɹˈæɾɪk kwˈɔːɹɾɚ, ʌnwˈɔːɹəntəbəl dɪmˈændz wɜː mˌeɪd əpˌɑːn ðə spˈeɪs pɹˈɑːpɚli ɐlˈɑːɾᵻd tə ðə fˈiːmeɪl fˈɛlənz,
+DUMMY1/LJ016-0274.wav|ænd ðə wˈɪndoʊz ʌvðɪ ˈɑːpəsˌɪt hˈaʊzɪz, wˌɪtʃ kəmˈændᵻd ɐ ɡˈʊd vjˈuː, æz jˈuːʒuːəl fˈɛtʃt hˈaɪ pɹˈaɪsᵻz.
+DUMMY1/LJ035-0014.wav|ɪt sˈaʊndᵻd hˈaɪ ænd ˈaɪ ɪmˈiːdɪətli kˈaɪnd ʌv lˈʊkt ˈʌp,
+DUMMY1/LJ033-0120.wav|wˌɪtʃ hiː bɪlˈiːvd wʌz wˌɛɹ ðə bˈæɡ ɹˈiːtʃt wɛn ɪt wʌz lˈeɪd ɑːnðə sˈiːt wɪð wˈʌn ˈɛdʒ ɐɡˈɛnst ðə dˈoːɹ.
+DUMMY1/LJ045-0015.wav|wˌɪtʃ dʒˈɑːnsən sˈɛd hiː dɪdnˌɑːt ɹɪsˈiːv ʌntˈɪl ˈæftɚ ðɪ ɐsˌæsᵻnˈeɪʃən. ðə lˈɛɾɚ sˈɛd ɪn pˈɑːɹt, kwˈoʊt,
+DUMMY1/LJ003-0299.wav|ðə lˈæɾɚɹ ˈɛnd ʌvðə nˈaɪntiːnθ sˈɛntʃɚɹi, sˈɛvɹəl ʌvwˈɪtʃ stˈɪl fˈɔːl fˈɑːɹ ʃˈɔːɹt ʌv ˌaʊɚɹ ˈɪŋɡlɪʃ aɪdˈiəl,
+DUMMY1/LJ032-0206.wav|ˈæftɚ kəmpˈɛɹɹɪŋ ðə ɹˈaɪfəl ɪnðə sˈɪmjʊlˌeɪɾᵻd fˈoʊɾəɡɹˌæf wɪððə ɹˈaɪfəl ɪn ɛɡzˈɪbɪt nˈʌmbɚ wˈʌn θˈɜːɾiθɹˈiː ˈeɪ, ʃˈeɪnaɪfəlt tˈɛstɪfˌaɪd, kwˈoʊt,
+DUMMY1/LJ028-0494.wav|bɪtwˌiːn ðə sˈɛvɹəl sˈɛkʃənz wɜː wˈaɪd spˈeɪsᵻz wˌɛɹ fˈʊt sˈoʊldʒɚz ænd tʃˌæɹiətˈɪɹz mˌaɪt fˈaɪt.
+DUMMY1/LJ005-0099.wav|ænd ɹɪpˈoːɹt æt lˈɛŋθ əpˌɑːn ðə kəndˈɪʃən ʌvðə pɹˈɪzənz ʌvðə kˈʌntɹi.
+DUMMY1/LJ015-0144.wav|dɪvˈɛləpt tʊ ɐ kəlˈɔsəl ɛkstˈɛnt ðə fɹˈɔːdz hiː hɐd ɔːlɹˌɛdi pɹˈæktɪst æz ɐ sʌbˈoːɹdᵻnət.
+DUMMY1/LJ019-0221.wav|ɪt wʌz ɪntˈɛndᵻd æz fˈɑːɹ æz pˈɑːsəbəl ðˈæt, ɛksˈɛpt ɐwˈeɪɾɪŋ tɹˈaɪəl, nˈoʊ pɹˈɪzənɚ ʃˌʊd fˈaɪnd hɪmsˈɛlf ɹˈɛlɪɡˌeɪɾᵻd tə nˈuːɡeɪt.
+DUMMY1/LJ003-0088.wav|ɪn wˈʌn, fɔːɹ sˈɛvən jˈɪɹz ðæt əvə mˈæn sˈɛntənst tə dˈɛθ, fɔːɹ hˈuːm ɡɹˈeɪt ˈɪntɹəst hɐdbɪn mˈeɪd, bˌʌt hˈuːm ɪt wʌz nˌɑːt θˈɔːt ɹˈaɪt tə pˈɑːɹdən.
+DUMMY1/LJ045-0216.wav|naɪntˈiːn sˈɪkstiθɹˈiː, mˈɪɹli tə dɪsˈɑːɹm hɜː ænd tə pɹəvˈaɪd ɐ dʒˌʌstɪfɪkˈeɪʃən ʌv sˈɔːɹts,
+DUMMY1/LJ042-0135.wav|ðæt hiː wʌz nˌɑːt jˈɛt twˈɛnti jˈɪɹz ˈoʊld wɛn hiː wɛnt tə ðə sˈoʊviət jˈuːniən wɪð sˈʌtʃ hˈaɪ hˈoʊps ænd nˌɑːt kwˈaɪt twˈɛntiθɹˈiː wɛn hiː ɹɪtˈɜːnd bˈɪɾɚli dˌɪsɐpˈɔɪntᵻd.
+DUMMY1/LJ049-0196.wav|ɑːnðɪ ˈʌðɚ hˈænd, ɪt ɪz ˈɜːdʒd ðæt ˈɔːl fˈiːtʃɚz ʌvðə pɹətˈɛkʃən ʌvðə pɹˈɛzɪdənt ænd hɪz fˈæmɪli ʃˌʊd biː kəmˈɪɾᵻd tʊ ɐn ɪlˈiːt ænd ˌɪndɪpˈɛndənt kˈɔːɹ.
+DUMMY1/LJ018-0278.wav|ðɪs wʌzðə wˈɛl ænd ɐstˈuːtli dɪvˈaɪzd plˈɑːt ʌvðə bɹˈʌðɚz bˈɪdwɛl,
+DUMMY1/LJ030-0238.wav|ænd ðˈɛn lˈʊkt ɐɹˈaʊnd ɐɡˈɛn ænd sˈɔː mˈoːɹ ʌv ðɪs mˈuːvmənt, ænd sˌoʊ ˈaɪ pɹəsˈiːdᵻd tə ɡˌoʊ tə ðə bˈæk sˈiːt ænd ɡɛt ˌɑːn tˈɑːp ʌv hˌɪm.
+DUMMY1/LJ018-0309.wav|wˌɛɹ pɹˈɑːbəbli ðə mˈʌni stˈɪl ɹɪmˈeɪnz.
+DUMMY1/LJ041-0199.wav|ɪz ʃˈoʊn mˈoʊst klˈɪɹli baɪ hɪz ɛmplˈɔɪmənt ɹɪlˈeɪʃənz ˈæftɚ hɪz ɹɪtˈɜːn fɹʌmðə sˈoʊviət jˈuːniən. ʌv kˈoːɹs, hiː mˌeɪd hɪz ɹˈiːəl pɹˈɑːbləmz wˈɜːs tə ðɪ ɛkstˈɛnt
+DUMMY1/LJ007-0076.wav|ðə lˈæks dˈɪsɪplˌɪn meɪntˈeɪnd ɪn nˈuːɡeɪt wʌz stˈɪl fˈɜːðɚ dɪtˈiəɹɪɹˌeɪɾᵻd baɪ ðə pɹˈɛzəns ʌv tˈuː ˈʌðɚ klˈæsᵻz ʌv pɹˈɪzənɚz hˌuː ˈɔːt nˈɛvɚ tə hɐvbɪn ��ɪnmeɪts ʌv sˈʌtʃ ɐ dʒˈeɪl.
+DUMMY1/LJ039-0118.wav|hiː hɐd hˈaɪ mˌoʊɾɪvˈeɪʃən. hiː hɐd pɹɪzˈuːməbli ɐ ɡˈʊd tʊ ˈɛksələnt ɹˈaɪfəl ænd ɡˈʊd ˌæmjuːnˈɪʃən.
+DUMMY1/LJ024-0019.wav|ænd ðɛɹ mˈeɪ biː ˈoʊnli nˈaɪn.
+DUMMY1/LJ008-0085.wav|ðə fˈaɪɚ hɐd nˌɑːt kwˈaɪt bˈɜːnt ˈaʊt æt twˈɛlv, ɪn nˌɪɹli fˈoːɹ ˈaɪʊɹz, ðæt ɪz tə sˈeɪ.
+DUMMY1/LJ018-0031.wav|ðɪs fˈɪkst ðə kɹˈaɪm pɹˈɪɾi sˈɜːtənli əpˌɑːn mˈʌlɚ, hˌuː hɐd ɔːlɹˌɛdi lˈɛft ðə kˈʌntɹi, ðˈʌs ɪnkɹˈiːsɪŋ səspˈɪʃən ˌʌndɚ wˌɪtʃ hiː lˈeɪ.
+DUMMY1/LJ030-0032.wav|dˈæləs pəlˈiːs stˈʊd æt ˈɪntɚvəlz ɐlˈɑːŋ ðə fˈɛns ænd dˈæləs plˈeɪn klˈoʊðz mˈɛn mˈɪkst ɪnðə kɹˈaʊd.
+DUMMY1/LJ050-0004.wav|dʒˈɛnɚɹəl suːpɚvˈɪʒən ʌvðə sˈiːkɹət sˈɜːvɪs
+DUMMY1/LJ039-0096.wav|ðɪs ɪz ɐ dˈɛfɪnət ɐdvˈæntɪdʒ tə ðə ʃˈuːɾɚ, ðə vˈiəkəl mˈuːvɪŋ dɚɹˈɛktli ɐwˈeɪ fɹʌm hˌɪm ænd ðə dˈaʊŋɡɹeɪd ʌvðə stɹˈiːt, ænd hiː bˌiːɪŋ ɪn ɐn ˈɛlɪvˌeɪɾᵻd pəzˈɪʃən
+DUMMY1/LJ041-0195.wav|ˈɑːswəldz ˈɪntɹəst ɪn mˈɑːɹksɪzəm lˈɛd sˌʌm pˈiːpəl tʊ ɐvˈɔɪd hˌɪm,
+DUMMY1/LJ047-0158.wav|ˈæftɚɹ ɐ mˈoʊmənts hˌɛsɪtˈeɪʃən, ʃiː tˈoʊld mˌiː ðæt hiː wˈɜːkt æt ðə tˈɛksəs skˈuːl bˈʊk dɪpˈɑːsɪtˌoːɹi nˌɪɹ ðə dˈaʊntaʊn ˈɛɹiə ʌv dˈæləs.
+DUMMY1/LJ050-0162.wav|ɪn plˈænɪŋ ɪts dˈeɪɾə pɹˈɑːsɛsɪŋ tɛknˈiːks,
+DUMMY1/LJ001-0051.wav|ænd pˈeɪɪŋ ɡɹˈeɪt ɐtˈɛnʃən tə ðə "pɹˈɛs wˈɜːk" ɔːɹ ˈæktʃuːəl pɹˈɑːsɛs ʌv pɹˈɪntɪŋ,
+DUMMY1/LJ028-0136.wav|ʌv ˈɔːl ðɪ ˈeɪnʃənt dɪskɹˈɪpʃənz ʌvðə fˈeɪməs wˈɔːlz ænd ðə sˈɪɾi ðeɪ pɹətˈɛktᵻd, ðæt ʌv hˈiəɹoʊdˌɑːɾəs ɪz ðə fˈʊləst.
+DUMMY1/LJ034-0134.wav|ʃˈɔːɹtli ˈæftɚ ðɪ ɐsˌæsᵻnˈeɪʃən bɹˈɛnən nˈoʊɾɪsd
+DUMMY1/LJ019-0348.wav|ˈɛvɹi fəsˈɪlɪɾi wʌz pɹˈɑːmɪsd. ðə sˈænkʃən ʌvðə sˈɛkɹətɹi ʌv stˈeɪt wʊd nˌɑːt biː wɪðhˈɛld ɪf plˈænz ænd ˈɛstᵻməts wɜː djˈuːli səbmˈɪɾᵻd,
+DUMMY1/LJ010-0219.wav|wˌaɪl wˈʌn stˈʊd ˌoʊvɚ ðə fˈaɪɚ wɪððə pˈeɪpɚz, ɐnˈʌðɚ stˈʊd wɪð lˈaɪɾᵻd tˈɔːɹtʃ tə fˈaɪɚ ðə hˈaʊs.
+DUMMY1/LJ011-0245.wav|mˈɪstɚ mˈʌleɪ kˈɔːld ɐɡˈɛn, tˈeɪkɪŋ wɪð hˌɪm fˈaɪv hˈʌndɹəd pˈaʊndz ɪn kˈæʃ. hˈaʊɚd dɪskˈʌvɚd ðˈɪs, ænd hɪz mˈænɚ wʌz vˈɛɹi səspˈɪʃəs;
+DUMMY1/LJ030-0035.wav|ˌɔːɹɡɐnaɪzˈeɪʃən ʌvðə mˈoʊɾɚkˌeɪd
+DUMMY1/LJ044-0135.wav|wˌaɪl hiː hɐd dɹˈɔːn sˌʌm ɐtˈɛnʃən tə hɪmsˈɛlf ænd hɐd ˈæktʃuːəli ɐpˈɪɹd ˌɑːn tˈuː ɹˈeɪdɪˌoʊ pɹˈoʊɡɹæmz, hiː hɐdbɪn ɐtˈækt baɪ kjˈuːbən ˈɛɡzaɪlz ænd ɐɹˈɛstᵻd,
+DUMMY1/LJ045-0090.wav|hiː wʌz vˈɛɹi mˈʌtʃ ˈɪntɹəstᵻd ɪn ˌɔːɾoʊbˌaɪəɡɹˈæfɪkəl wˈɜːks ʌv aʊtstˈændɪŋ stˈeɪtsmɛn ʌvðə juːnˈaɪɾᵻd stˈeɪts, tə hˈuːm hɪz wˈaɪf θˈɔːt hiː kəmpˈɛɹd hɪmsˈɛlf.
+DUMMY1/LJ026-0034.wav|wˌɛn ˌɛni ɡˈɪvən "pɹˈɑːɾɪst" hɐz təbi klˈæsɪfˌaɪd ðə kˈeɪs mˈʌst biː dᵻsˈaɪdᵻd ˌɑːn ɪts ˌɪndɪvˈɪdʒuːəl mˈɛɹɪts;
+DUMMY1/LJ045-0092.wav|æz tə ðə fˈækt ðæt hiː wʌz ɐn aʊtstˈændɪŋ mˈæn, ˈɛnd kwˈoʊt.
+DUMMY1/LJ017-0050.wav|pˈɑːmɚ, hˌuː wʌz ˈoʊnli θˈɜːɾiwˈʌn æt ðə tˈaɪm ʌv hɪz tɹˈaɪəl, wʌz ɪn ɐpˈɪɹəns ʃˈɔːɹt ænd stˈaʊt, wɪð ɐ ɹˈaʊnd hˈɛd
+DUMMY1/LJ036-0104.wav|wˈeɪli pˈɪkt ˈɑːswəld.
+DUMMY1/LJ019-0055.wav|hˈaɪ ɐθˈɔːɹɪɾiz wɜːɹ ɪn fˈeɪvɚɹ ʌv kəntˈɪnjuːəs sˌɛpɚɹˈeɪʃən.
+DUMMY1/LJ010-0030.wav|ðə bɹˈuːɾəl fəɹˈɑːsɪɾi ʌvðə wˈaɪld bˈiːst wˈʌns ɐɹˈaʊzd, ðə sˈeɪm mˈiːnz, ðə sˈeɪm wˈɛpənz wɜːɹ ɛmplˈɔɪd tə dˈuː ðə dɹˈɛdfəl dˈiːd,
+DUMMY1/LJ038-0047.wav|sˌʌm ʌvðɪ ˈɑːfɪsɚz sˈɔː ˈɑːswəld stɹˈaɪk məkdˈɑːnəld wɪð hɪz fˈɪst. mˈoʊst əv ðˌɛm hˈɜːd ɐ klˈɪk wˌɪtʃ ðeɪ ɐsˈuːmd təbi ɐ klˈɪk ʌvðə hˈæmɚɹ ʌvðə ɹɪvˈɑːlvɚ.
+DUMMY1/LJ009-0074.wav|lˈɛt ˌʌs pˈæs ˈɑːn.
+DUMMY1/LJ048-0069.wav|ˈɛfɚts mˌeɪd baɪ ðə bjˈʊɹɹoʊ sˈɪns ðɪ ɐsˌæsᵻnˈeɪʃən, ɑːnðɪ ˈʌðɚ hˈænd,
+DUMMY1/LJ003-0211.wav|ðeɪ wɜː nˈɛvɚ lˈɛft kwˈaɪt ɐlˈoʊn fɔːɹ fˈɪɹ ʌv sˈuːɪsˌaɪd, ænd fɚðə sˈeɪm ɹˈiːzən ðeɪ wɜː sˈɜːtʃt fɔːɹ wˈɛpənz ɔːɹ pˈɔɪzənz.
+DUMMY1/LJ048-0053.wav|ɪt ɪz ðə kənklˈuːʒən ʌvðə kəmˈɪʃən ðˈæt, ˈiːvən ɪnðɪ ˈæbsəns ʌv sˈiːkɹət sˈɜːvɪs kɹaɪtˈiəɹɪə
+DUMMY1/LJ033-0093.wav|fɹˈeɪzɪɚɹ ˈɛstᵻmˌeɪɾᵻd ðætðə bˈæɡ wʌz tˈuː fˈiːt lˈɑːŋ, kwˈoʊt, ɡˈɪv ænd tˈeɪk ɐ fjˈuː ˈɪntʃᵻz, ˈɛnd kwˈoʊt, ænd ɐbˌaʊt fˈaɪv ɔːɹ sˈɪks ˈɪntʃᵻz wˈaɪd.
+DUMMY1/LJ006-0149.wav|ðə tˈɜːnkiːz lˈɛft ðə pɹˈɪzənɚz vˈɛɹi mˈʌtʃ tə ðɛmsˈɛlvz, nˈɛvɚɹ ˈɛntɚɹɪŋ ðə wˈɔːɹdz ˈæftɚ lˈɑːkɪŋˌʌp tˈaɪm, æt dˈʌsk, tˈɪl ʌnlˈɑːkɪŋ nˈɛkst mˈɔːɹnɪŋ,
+DUMMY1/LJ018-0211.wav|ðə fˈɑːls kˈɔɪn wʌz bˈɔːt baɪ ɐn ˈeɪdʒənt fɹʌm ɐn ˈeɪdʒənt, ænd dˈiːlɪŋz wɜː kˈæɹɪd ˌɑːn sˈiːkɹətli æt ðə "klˈɑːk hˈaʊs" ɪn sˈɛvən dˈaɪəlz.
+DUMMY1/LJ008-0054.wav|ðɪs kəntɹˈaɪvəns ɐpˈɪɹz tə hɐvbɪn kˈɑːpɪd wɪð ɪmpɹˈuːvmənts fɹʌm ðæt wˌɪtʃ hɐdbɪn jˈuːzd ɪn dˈʌblɪn æɾə stˈɪl ˈɜːlɪɚ dˈeɪt,
+DUMMY1/LJ040-0052.wav|ðæt hɪz kəmˈɪtmənt tə mˈɑːɹksɪzəm wʌz ɐn ɪmpˈoːɹtənt fˈæktɚɹ ˈɪnfluːənsɪŋ hɪz kˈɑːndʌkt dˈʊɹɪŋ hɪz ɐdˈʌlt jˈɪɹz.
+DUMMY1/LJ028-0023.wav|tˈuː wˈiːks pˈæs, ænd æt lˈæst juː stˈænd ɑːnðɪ ˈiːstɚn ˈɛdʒ ʌvðə plætˈoʊ
+DUMMY1/LJ009-0184.wav|lˈɔːɹd fˈɜːɹɚz bˈɑːdi wʌz bɹˈɔːt tə sˈɜːdʒənz hˈɔːl ˈæftɚɹ ˌɛksɪkjˈuːʃən ɪn hɪz ˈoʊn kˈæɹɪdʒ ænd sˈɪks;
+DUMMY1/LJ005-0252.wav|ɐ kəmˈɪɾi wʌz ɐpˈɔɪntᵻd, ˌʌndɚ ðə pɹˈɛzɪdənsi ʌvðə dˈuːk ʌv ɹˈɪtʃmənd
+DUMMY1/LJ015-0266.wav|hɐz pɹˈɑːbəbli nˈoʊ pˈæɹəlˌɛl ɪnðɪ ˈænəlz ʌv kɹˈaɪm. sˈæwɚd hɪmsˈɛlf ɪz ɐ stɹˈaɪkɪŋ ænd ɪn sˌʌm ɹɪspˈɛkts ɐn juːnˈiːk fˈɪɡjɚɹ ɪn kɹˈɪmɪnəl hˈɪstɚɹi.
+DUMMY1/LJ017-0059.wav|ˈiːvən ˈæftɚ sˈɛntəns, ænd ʌntˈɪl wɪðˌɪn ɐ fjˈuː ˈaɪʊɹz ʌv ˌɛksɪkjˈuːʃən, hiː wʌz bˈɔɪd ˌʌp wɪððə hˈoʊp ʌv ɹɪpɹˈiːv.
+DUMMY1/LJ024-0034.wav|wˌʌt dˈuː ðeɪ mˈiːn baɪ ðə wˈɜːdz "pˈækɪŋ ðə kˈoːɹt"?
+DUMMY1/LJ016-0089.wav|hiː wʌz ɛnɡˈeɪdʒd ɪn wˈaɪtwɑːʃɪŋ ænd klˈiːnɪŋ; ðɪ ˈɑːfɪsɚ hˌuː hɐd hˌɪm ɪn tʃˈɑːɹdʒ lˈɛft hˌɪm ɑːnðə stˈɛɹz lˈiːdɪŋ tə ðə ɡˈælɚɹi.
+DUMMY1/LJ039-0227.wav|wɪð tˈuː hˈɪts, wɪðˌɪn fˈoːɹ pˈɔɪnt ˈeɪt ænd fˈaɪv pˈɔɪnt sˈɪks sˈɛkəndz.
+DUMMY1/LJ001-0096.wav|hæv nˈaʊ kˈʌm ˌɪntʊ dʒˈɛnɚɹəl jˈuːs ænd ɑːɹ ˈɑːbvɪəsli ɐ ɡɹˈeɪt ɪmpɹˈuːvmənt ɑːnðɪ ˈɔːɹdɪnˌɛɹi "mˈɑːdɚn stˈaɪl" ɪn jˈuːs ɪn ˈɪŋɡlənd, wˌɪtʃ ɪz ɪn fˈækt ðə bədˈoʊni tˈaɪp
+DUMMY1/LJ018-0129.wav|hˌuː θɹˈɛʔn̩d tə bɪtɹˈeɪ ðə θˈɛft. bˌʌt bɹˈuːɚ, ˈiːðɚ bɪfˌoːɹ ɔːɹ ˈæftɚ ðˈɪs, səkˈʌmd tə tɛmptˈeɪʃən,
+DUMMY1/LJ010-0157.wav|ænd ðˈæt, æz hiː wʌz stˈɑːɹvɪŋ, hiː hɐd ɹɪzˈɑːlvd ˌɑːn ðɪs dˈɛspɚɹət dˈiːd,
+DUMMY1/LJ038-0264.wav|hiː kənklˈuːdᵻd ðˈæt, kwˈoʊt, ðə dʒˈɛnɚɹəl ɹˈaɪflɪŋ kˌæɹɪktɚɹˈɪstɪks ʌvðə ɹˈaɪfəl ɑːɹ ʌvðə sˈeɪm tˈaɪp æz ðoʊz fˈaʊnd ɑːnðə bˈʊlɪt
+DUMMY1/LJ031-0165.wav|wˌɛn sɪkjˈʊɹɪɾi ɐɹˈeɪndʒmənts æt ðɪ ˈɛɹpoːɹt wɜː kəmplˈiːt, ðə sˈiːkɹət sˈɜːvɪs mˌeɪd ðə nˈɛsəsɚɹi ɐɹˈeɪndʒmənts fɚðə vˈaɪs pɹˈɛzɪdənt tə lˈiːv ðə hˈɑːspɪɾəl.
+DUMMY1/LJ018-0244.wav|ðɪ ɪfˈɛkt ʌv ɪstˈæblɪʃɪŋ ðə fˈɔːɹdʒɚɹiz wʊd biː tə ɹɪstˈoːɹ tə ðə ɹˈuːpɛl fˈæmɪli lˈændz fɔːɹ wˌɪtʃ ɐ pɹˈaɪs hɐd ɔːlɹˌɛdi bˌɪn pˈeɪd
+DUMMY1/LJ007-0071.wav|ɪnðə fˈeɪs ʌv ɪmpˈɛdɪmənts kənfˈɛsɪdli dɪskˈɜːɹɪdʒɪŋ
+DUMMY1/LJ028-0340.wav|sˈʌtʃ ʌvðə bˌæbɪlˈoʊniənz æz wˈɪtnəst ðə tɹˈɛtʃɚɹi tˈʊk ɹˈɛfjuːdʒ ɪnðə tˈɛmpəl ʌv dʒˈuːpɪɾɚ bɪlˈuːz;
+DUMMY1/LJ017-0164.wav|wɪððɪ aɪdˈiə ʌv sʌbdʒˈɛktɪŋ hɜː tə ðɪ ˈɪɹɪtənt pˈɔɪzən slˈoʊli bˌʌt ʃˈʊɹli ʌntˈɪl ðə dɪzˈaɪɚd ɪfˈɛkt, dˈɛθ, wʌz ɐtʃˈiːvd.
+DUMMY1/LJ048-0197.wav|ˈaɪ ðˈɛn tˈoʊld ðɪ ˈɑːfɪsɚz ðæt ðɛɹ pɹˈaɪmɚɹi dˈuːɾi wʌz tɹˈæfɪk ænd kɹˈaʊd kəntɹˈoʊl ænd ðæt ðeɪ ʃˌʊd biː ɐlˈɜːt fɔːɹ ˌɛni pˈɜːsənz hˌuː mˌaɪt ɐtˈɛmpt tə θɹˈoʊ ˈɛnɪθˌɪŋ
+DUMMY1/LJ013-0098.wav|mˈɪstɚɹ ˈɑːksənfɚd hˌævɪŋ dɪnˈaɪd ðæt hiː hɐd mˌeɪd ˌɛni tɹˈænsfɜːɹ ʌv stˈɑːk, ðə mˈæɾɚ wʌz ɐtwˈʌns pˌʊt ˌɪntʊ ðə hˈændz ʌvðə pəlˈiːs.
+DUMMY1/LJ012-0049.wav|lˈɛd hˌɪm tə θˈɪŋk sˈiəɹɪəsli ʌv tɹˈaɪɪŋ hɪz fˈɔːɹtʃənz ɪn ɐnˈʌðɚ lˈænd.
+DUMMY1/LJ030-0014.wav|kwˈoʊt, ðætðə kɹˈaʊd wʌz ɐbˌaʊt ðə sˈeɪm æz ðə wˈʌn wˌɪtʃ kˈeɪm tə sˈiː hˌɪm bɪfˌoːɹ bˌʌt ðɛɹwˌɜː wˈʌn hˈʌndɹəd θˈaʊzənd ˈɛkstɹə pˈiːpəl ˌɑːn hˈænd hˌuː kˈeɪm tə sˈiː mɪsˈɛs kˈɛnədi.
+DUMMY1/LJ014-0186.wav|ɐ mˈɪlɪnɚz pˈoːɹɾɚ,
+DUMMY1/LJ015-0027.wav|jˈɛt ˈiːvən sˌoʊ ˈɜːli æz ðə dˈɛθ ʌvðə fˈɜːst sˌɜː dʒˈɑːn pˈɔːl,
+DUMMY1/LJ047-0049.wav|mɚɹˈiːnə ˈɑːswəld, haʊˈɛvɚ, ɹɪkˈɔːld ðæt hɜː hˈʌsbənd wʌz ʌpsˈɛt baɪ ðɪs ˈɪntɚvjˌuː.
+DUMMY1/LJ012-0021.wav|æt foːɹtˈiːn hiː wʌzɐ pˈɪkpɑːkɪt ænd ˈeɪ "dˈʌfɚ," ɔːɹ ɐ sˈɛlɚɹ ʌv ʃˈæm ɡˈʊdz.
+DUMMY1/LJ003-0140.wav|ˈʌðɚwˌaɪz hiː wʊdhɐv bˌɪn stɹˈɪpt ʌv hɪz klˈoʊðz. ˈɛnd kwˈoʊt.
+DUMMY1/LJ042-0130.wav|ʃˈɔːɹtli ðɛɹˈæftɚ, lˈɛs ðɐn eɪtˈiːn mˈʌnθs ˈæftɚ hɪz dɪfˈɛkʃən, ɐbˌaʊt sˈɪks wˈiːks bɪfˌoːɹ hiː mˈɛt mɚɹˈiːnə pɹˌuːsɐkˈoʊvə,
+DUMMY1/LJ019-0180.wav|hɪz lˈɛɾɚ tə ðə kˌɔːɹpɚɹˈeɪʃən, ˌʌndɚ dˈeɪt fˈoːɹθ dʒjˈuːn,
+DUMMY1/LJ017-0108.wav|hiː wʌz stɹˈʌk wɪððɪ ɐpˈɪɹəns ʌvðə kˈɔːɹps, wˌɪtʃ wʌz nˌɑːt iːmˈeɪsɪˌeɪɾᵻd, æz ˈæftɚɹ ɐ lˈɑːŋ dɪzˈiːz ˈɛndɪŋ ɪn dˈɛθ;
+DUMMY1/LJ006-0268.wav|wˈɪmɪn sˈɔː mˈɛn ɪf ðeɪ mˈɪɹli pɹɪtˈɛndᵻd təbi wˈaɪvz; ˈiːvən bˈɔɪz wɜː vˈɪzɪɾᵻd baɪ ðɛɹ swˈiːthɑːɹts.
+DUMMY1/LJ044-0125.wav|ʌv ɹˈɛzɪdəns ɪnðə jˈuː.ˈɛs.ˈɛs.ˈɑːɹ. ɐɡˈɛnst ˌɛni kˈɔːz wˌɪtʃ ˈaɪ dʒˈɔɪn, baɪ ɐsˈoʊsɪˈeɪʃən,
+DUMMY1/LJ015-0231.wav|ɪt wʌz tˈɛstɚz bˈɪznəs, hˌuː hɐd ˈæksɛs tə ðə ɹˈeɪlweɪ kˈʌmpəniz bˈʊks, tə wˈɑːtʃ fɔːɹ ðˈɪs.
+DUMMY1/LJ002-0225.wav|ðə ɹˈɛntəlz ʌv ɹˈuːmz ænd fˈiːz wɛnt tə ðə wˈɔːɹdən, hˌuːz ˈɪnkʌm wʌz tˈuː θˈaʊzənd θɹˈiː hˈʌndɹəd sˈɛvəntitˈuː pˈaʊndz.
+DUMMY1/LJ034-0072.wav|ðɪ ɛmplˈɔɪiːz ɹˈeɪsd ðɪ ˈɛlɪvˌeɪɾɚz tə ðə fˈɜːst flˈoːɹ. ɡˈɪvənz sˈɔː ˈɑːswəld stˈændɪŋ æt ðə ɡˈeɪt ɑːnðə fˈɪfθ flˈoːɹ æz ðɪ ˈɛlɪvˌeɪɾɚ wɛnt bˈaɪ.
+DUMMY1/LJ045-0033.wav|hiː bɪɡˈæn tə tɹˈiːt mˌiː bˈɛɾɚ. hiː hˈɛlpt mˌiː mˈoːɹ ɑːlðˈoʊ hiː ˈɔːlweɪz dˈɪd hˈɛlp. bˌʌt hiː wʌz mˈoːɹ ɐtˈɛntɪv, ˈɛnd kwˈoʊt.
+DUMMY1/LJ031-0058.wav|tʊ ɪnfjˈuːz blˈʌd ænd flˈuːɪdz ˌɪntʊ ðə sˈɜːkjʊlətˌoːɹi sˈɪstəm.
+DUMMY1/LJ029-0197.wav|dˈʊɹɪŋ noʊvˈɛmbɚ ðə dˈæləs pˈeɪpɚz ɹɪpˈoːɹɾᵻd fɹˈiːkwəntli ɑːnðə plˈænz fɔːɹ pɹətˈɛktɪŋ ðə pɹˈɛzɪdənt, stɹˈɛsɪŋ ðə θˈʌɹoʊnəs ʌvðə pɹˌɛpɚɹˈeɪʃənz.
+DUMMY1/LJ043-0047.wav|ˈɑːswəld ænd hɪz fˈæmɪli lˈɪvd fɚɹə bɹˈiːf pˈiəɹɪəd wɪð hɪz mˈʌðɚɹ æt hɜːɹ ˈɜːdʒɪŋ, bˌʌt ˈɑːswəld sˈuːn dᵻsˈaɪdᵻd tə mˈuːv ˈaʊt.
+DUMMY1/LJ021-0026.wav|sˈiːmz nˈɛsəsɚɹi tə pɹədˈuːs ðə sˈeɪm ɹɪzˈʌlt ʌv dʒˈʌstɪs ænd ɹˈaɪt kˈɑːndʌkt
+DUMMY1/LJ003-0230.wav|ðə pɹˈɪzən ɐlˈaʊənsᵻz wɜːɹ ˈiːkt ˈaʊt baɪ ðə bɹˈoʊkən vˈɪktʃuːəlz dʒˈɛnɚɹəsli ɡˈɪvən baɪ sˈɛvɹəl ˈiːɾɪŋhˈaʊs kˈiːpɚz ɪnðə sˈɪɾi,
+DUMMY1/LJ037-0252.wav|tˈɛd kˈæləwˌeɪ, hˌuː sˈɔː ðə ɡˈʌnmən mˈoʊmənts ˈæftɚ ðə ʃˈuːɾɪŋ, tˈɛstɪfˌaɪd ðæt kəmˈɪʃən ɛɡzˈɪbɪt nˈʌmbɚ wˈʌn sˈɪkstitˈuː
+DUMMY1/LJ031-0008.wav|mˈiːnwaɪl, tʃˈiːf kˈɜːɹi ˈɔːɹdɚd ðə pəlˈiːs bˈeɪs stˈeɪʃən tə nˈoʊɾɪfˌaɪ pˈɑːɹklənd hˈɑːspɪɾəl ðætðə wˈuːndᵻd pɹˈɛzɪdənt wʌz ɑːn ɹˈuːt.
+DUMMY1/LJ030-0021.wav|ˈɔːl wˈʌn hædtə dˈuː wʌz ɡɛt ɐ hˈaɪ bˈɪldɪŋ sˈʌmdeɪ wɪð ɐ tˌɛlɪskˈɑːpɪk ɹˈaɪfəl, ænd ðɛɹwˌʌz nˈʌθɪŋ ˈɛnɪbˌɑːdi kʊd dˈuː tə dɪfˈɛnd ɐɡˈɛnst sˈʌtʃ ɐn ɐtˈɛmpt.
+DUMMY1/LJ046-0179.wav|bˌiːɪŋ ɹɪvjˈuːd ɹˈɛɡjuːlɚli.
+DUMMY1/LJ025-0118.wav|ænd ðˈæt, haʊˈɛvɚ daɪvˈɜːs mˈeɪ biː ðə fˈæbɹɪks ɔːɹ tˈɪʃuːz ʌvwˈɪtʃ ðɛɹ bˈɑːdɪz ɑːɹ kəmpˈoʊzd, ˈɔːl ðiːz vˈɛɹid stɹˈʌktʃɚz ɹɪzˈʌlt
+DUMMY1/LJ028-0278.wav|zˈɑːpɪɹəs, wˌɛn ðeɪ tˈoʊld hˌɪm, nˌɑːt θˈɪŋkɪŋ ðˌɐɾɪt kʊd biː tɹˈuː, wɛnt ænd sˈɔː ðə kˈoʊlt wɪð hɪz ˈoʊn ˈaɪz;
+DUMMY1/LJ007-0090.wav|nˌɑːt ˈoʊnli dˈɪd ðɛɹ pɹˈɛzəns tˈɛnd ɡɹˈeɪtli tʊ ˌɪntəfˈɪɹ wɪððə dˈɪsɪplˌɪn ʌvðə pɹˈɪzən, bˌʌt ðɛɹ kəndˈɪʃən wʌz dɪplˈoːɹəbəl ɪnðɪ ɛkstɹˈiːm.
+DUMMY1/LJ045-0045.wav|ðæt ʃiː wʊd biː ˈeɪbəl tə lˈiːv ðə sˈoʊviət jˈuːniən. mɚɹˈiːnə ˈɑːswəld hɐz dɪnˈaɪd ðˈɪs.
+DUMMY1/LJ028-0289.wav|fɔːɹ hiː kˈʌt ˈɔf hɪz ˈoʊn nˈoʊz ænd ˈɪɹz, ænd ðˈɛn, klˈɪpɪŋ hɪz hˈɛɹ klˈoʊs ænd flˈɑːɡɪŋ hɪmsˈɛlf wɪð ɐ skˈɜːdʒ,
+DUMMY1/LJ009-0276.wav|kˈælkɹæft, ðə mˈoʊmənt hiː hɐd ɐdʒˈʌstᵻd ðə kˈæp ænd ɹˈoʊp, ɹˈæn dˌaʊn ðə stˈɛps, dɹˈuː ðə bˈoʊlt, ænd dˌɪsɐpˈɪɹd.
+DUMMY1/LJ031-0122.wav|tɹˈiːɾᵻd ðə ɡˈʌnʃɑːt wˈuːnd ɪnðə lˈɛft θˈaɪ.
+DUMMY1/LJ016-0205.wav|hiː ɹɪsˈiːvd ɐ ɹɪtˈeɪnɪŋ fˈiː ʌv fˈaɪv pˈaʊndz, fˈaɪv ʃˈɪlɪŋz, wɪððə jˈuːʒuːəl ɡˈɪni fɔːɹ ˈiːtʃ dʒˈɑːb;
+DUMMY1/LJ019-0248.wav|lˈiːdɪŋ tʊ ɐn ɪniːkwˈɑːlɪɾi, ʌnsˈɜːtənti, ænd ɪnɪfˈɪʃənsi ʌv pˈʌnɪʃmənt pɹədˈʌktɪv ʌvðə mˈoʊst pɹˌɛdʒuːdˈɪʃəl ɹɪzˈʌlts.
+DUMMY1/LJ033-0183.wav|ɪt wʌz nˌɑːt sɚpɹˈaɪzɪŋ ðætðə ɹˈɛplɪkə sˈæk mˌeɪd ˌɑːn dᵻsˈɛmbɚ wˌʌn, naɪntˈiːn sˈɪkstiθɹˈiː,
+DUMMY1/LJ037-0001.wav|ɹɪpˈoːɹt ʌvðə pɹˈɛzɪdənts kəmˈɪʃən ɑːnðɪ ɐsˌæsᵻnˈeɪʃən ʌv pɹˈɛzɪdənt kˈɛnədi. ðə wˈɔːɹən kəmˈɪʃən ɹɪpˈoːɹt. baɪ ðə pɹˈɛzɪdənts kəmˈɪʃən ɑːnðɪ ɐsˌæsᵻnˈeɪʃən ʌv pɹˈɛzɪdənt kˈɛnədi.
+DUMMY1/LJ018-0218.wav|ɪn eɪtˈiːn fˈɪftifˈaɪv
+DUMMY1/LJ001-0102.wav|hˈɪɹ ənd ðˈɛɹ ɐ bˈʊk ɪz pɹˈɪntᵻd ɪn fɹˈæns ɔːɹ dʒˈɜːməni wɪð sˌʌm pɹɪtˈɛnʃən tə ɡˈʊd tˈeɪst,
+DUMMY1/LJ007-0125.wav|ɪt wʌz daɪvˈɜːɾᵻd fɹʌm ɪts pɹˈɑːpɚ jˈuːsᵻz, ˈænd, æz ðˈə "plˈeɪs ʌvðə ɡɹˈeɪɾəst kˈʌmfɚt," wʌz ɐlˈɑːɾᵻd tə pˈɜːsənz hˌuː ʃˌʊd nˌɑːɾɐv bˌɪn sˈɛnt tə nˈuːɡeɪt æt ˈɔːl.
+DUMMY1/LJ050-0022.wav|ɐ fˈɔːɹməl ænd θˈʌɹoʊ dɪskɹˈɪpʃən ʌvðə ɹɪspˌɑːnsəbˈɪlɪɾiz ʌvðɪ ɐdvˈæns ˈeɪdʒənt ɪz nˈaʊ ɪn pɹˌɛpɚɹˈeɪʃən baɪ ðə sˈɜːvɪs.
+DUMMY1/LJ028-0212.wav|ɑːnðə nˈaɪt ʌvðɪ ɪlˈɛvənθ dˈeɪ ɡˈɑːbɹiəz kˈɪld ðə sˈʌn ʌvðə kˈɪŋ.
+DUMMY1/LJ028-0357.wav|jˈɛt wiː mˈeɪ biː ʃˈʊɹ ðæt bˈæbɪlən wʌz tˈeɪkən baɪ dˈɛɹɪəs ˈoʊnli baɪ jˈuːs ʌv stɹˈæɾeɪdʒəm. ɪts wˈɔːlz wɜːɹ ɪmpɹˈɛɡnəbəl.
+DUMMY1/LJ014-0199.wav|ðɛɹwˌʌz nˈoʊ kˈeɪs tə mˌeɪk ˈaʊt; wˌaɪ wˈeɪst mˈʌni ˌɑːn lˈɔɪɚz fɚðə dɪfˈɛns? hɪz dɪmˈiːnɚ wʌz kˈuːl ænd kəlˈɛktᵻd θɹuːˈaʊt;
+DUMMY1/LJ016-0077.wav|ɐ mˈæn nˈeɪmd lˈɪɹz, ˌʌndɚ sˈɛntəns ʌv tɹænspoːɹtˈeɪʃən fɚɹən ɐtˈɛmpt æt mˈɜːdɚɹ ˌɑːn bˈoːɹd ʃˈɪp, ɡɑːt ˌʌp pˈɑːɹt ʌvðə wˈeɪ,
+DUMMY1/LJ009-0194.wav|ænd ðæt ɛɡzˈɛkjuːɾɚz ɔːɹ pˈɜːsənz hˌævɪŋ lˈɔːfəl pəzˈɛʃən ʌvðə bˈɑːdɪz
+DUMMY1/LJ014-0094.wav|dɪskˈʌvɚɹi ʌvðə mˈɜːdɚ kˈeɪm ɪn ðɪs wˈaɪz. oʊkˈɑːnɚ, ɐ pˈʌŋktʃuːəl ænd wˈɛlkəndˈʌktᵻd əfˈɪʃəl, wʌz ɐtwˈʌns mˈɪst æt ðə lˈʌndən dˈɑːks.
+DUMMY1/LJ001-0079.wav|kˈæslɑːnz tˈaɪp ɪz klˈɪɹ ænd nˈiːt, ænd fˈɛɹli wˈɛl dɪzˈaɪnd;
+DUMMY1/LJ026-0052.wav|ɪnðə njuːtɹˈɪʃən ʌvðɪ ˈænɪməl ðə mˈoʊst ɪsˈɛnʃəl ænd kˌæɹɪktɚɹˈɪstɪk pˈɑːɹt ʌvðə fˈuːd səplˈaɪ ɪz dɪɹˈaɪvd fɹʌm vˈɛdʒɪɾəbəl
+DUMMY1/LJ013-0005.wav|wˈʌn ʌvðɪ ˈɜːlɪəst ʌvðə bˈɪɡ ˈɑːpɚɹˌeɪɾɚz ɪn fɹˈɔːdʒuːlənt fˈaɪnæns wʌz ˈɛdwɚd bˈoʊmɑːnt smˈɪθ,
+DUMMY1/LJ033-0072.wav|ˈaɪ ðˈɛn stˈɛpt ˈɔf ʌv ɪt ænd ðɪ ˈɑːfɪsɚ pˈɪkt ɪt ˌʌp ɪnðə mˈɪdəl ænd ɪt bˈɛnt sˈoʊ.
+DUMMY1/LJ036-0067.wav|ɐkˈoːɹdɪŋ tə məkwˈæɾɚz, ðə bˈɛkli bˈʌs wʌz bɪhˌaɪnd ðə mɑːɹsˈɑːliz bˈʌs, bˌʌt hiː dɪdnˌɑːt ˈæktʃuːəli sˈiː ɪt.
+DUMMY1/LJ025-0098.wav|ænd ɪt ɪz pɹˈɑːbəbəl ðæt ˈæmɪlˌɔɪd sˈʌbstənsᵻz ɑːɹ jˌuːnɪvˈɜːsəli pɹˈɛzənt ɪnðɪ ˈænɪməl ˈɔːɹɡənˌɪzəm, ðˌoʊ nˌɑːt ɪnðə pɹɪsˈaɪs fˈɔːɹm ʌv stˈɑːɹtʃ.
+DUMMY1/LJ005-0257.wav|dˈʊɹɪŋ wˌɪtʃ tˈaɪm ɐ hˈoʊst ʌv wˈɪtnəsᵻz wɜːɹ ɛɡzˈæmɪnd, ænd ðə kəmˈɪɾi pɹɪzˈɛntᵻd θɹˈiː sˈɛpɹət ɹɪpˈoːɹts,
+DUMMY1/LJ004-0024.wav|ðˈʌs ɪn eɪtˈiːn θɜːtˈiːn ðɪ ɛɡzˈækʃən ʌv dʒˈeɪl fˈiːz hɐdbɪn fəbˈɪdən baɪ lˈɔː,
+DUMMY1/LJ049-0154.wav|ɪn eɪtˈiːn nˈaɪntifˈoːɹ,
+DUMMY1/LJ039-0059.wav| θɹˈiː hɪz ɛkspˈiəɹɪəns ænd pɹˈæktɪs ˈæftɚ lˈiːvɪŋ ðə mɚɹˈiːn kˈɔːɹ, ænd fˈoːɹ ðɪ ˈækjʊɹəsi ʌvðə wˈɛpən ænd ðə kwˈɑːlɪɾi ʌvðɪ ˌæmjuːnˈɪʃən.
+DUMMY1/LJ007-0150.wav|hiː ɪz ɐlˈaʊd ˌɪntɚkˈoːɹs wɪð pɹˈɑːstɪtˌuːts hˈuː, ɪn nˈaɪn kˈeɪsᵻz ˌaʊɾəv tˈɛn, hæv ɚɹˈɪdʒɪnəli kəndˈuːst tə hɪz ɹˈuːɪn;
+DUMMY1/LJ015-0001.wav|kɹˈɑːnɪkəlz ʌv nˈuːɡeɪt, vˈɑːljuːm tˈuː. baɪ ˈɑːɹθɚ ɡɹˈɪfɪθs. sˈɛkʃən eɪtˈiːn: nˈuːɡeɪt nˌoʊɾoːɹˈaɪəɾɪz kəntˈɪnjuːd, pˈɑːɹt θɹˈiː.
+DUMMY1/LJ010-0158.wav|fˈiːlɪŋ, æz hiː sˈɛd, ðæt hiː mˌaɪt æz wˈɛl biː ʃˈɑːt ɔːɹ hˈæŋd æz ɹɪmˈeɪn ɪn sˈʌtʃ ɐ stˈeɪt.
+DUMMY1/LJ010-0281.wav|hˌuː hɐd bˈoːɹn ðə kwˈiːnz kəmˈɪʃən, fˈɜːst æz kˈɔːɹnɪt, ænd ðˈɛn luːtˈɛnənt, ɪnðə tˈɛnθ hʌzˈɑːɹz.
+DUMMY1/LJ033-0055.wav|ænd hiː kʊd dˌɪsɐsˈɛmbəl ɪt mˈoːɹ ɹˈæpɪdli.
+DUMMY1/LJ015-0218.wav|ɐ nˈuː ɐkˈɑːmplɪs wʌz nˈaʊ nˈiːdᵻd wɪðˌɪn ðə kˈʌmpəniz ɪstˈæblɪʃmənt, ænd pˈɪɹs lˈʊkt ɐbˌaʊt lˈɑːŋ bɪfˌoːɹ hiː fˈaʊnd ðə ɹˈaɪt pˈɜːsən.
+DUMMY1/LJ027-0006.wav|ɪn ˈɔːl ðiːz lˈaɪnz ðə fˈækts ɑːɹ dɹˈɔːn təɡˌɛðɚ baɪ ɐ stɹˈɔŋ θɹˈɛd ʌv jˈuːnɪɾi.
+DUMMY1/LJ016-0049.wav|hiː hɐd hˈɪɹ kəmplˈiːɾᵻd hɪz ɐsˈɛnt.
+DUMMY1/LJ006-0088.wav|ɪt wʌz nˌɑːt lˈaɪkli ðˌæɾə sˈɪstəm wˌɪtʃ lˈɛft ˈɪnəsənt mˈɛn fɚðə ɡɹˈeɪt bˈʌlk ʌv nˈuː ɐɹˈaɪvəlz wɜː stˈɪl ʌntɹˈaɪd
+DUMMY1/LJ042-0133.wav|ɐ ɡɹˈeɪt tʃˈeɪndʒ mˈʌstɐv əkˈɜːd ɪn ˈɑːswəldz θˈɪŋkɪŋ tʊ ɪndˈuːs hˌɪm tə ɹɪtˈɜːn tə ðə juːnˈaɪɾᵻd stˈeɪts.
+DUMMY1/LJ045-0234.wav|wˌaɪl hiː dˈɪd bɪkˌʌm ɛnɹˈeɪdʒd æt æt lˈiːst wˈʌn pˈɔɪnt ɪn hɪz ɪntˌɛɹəɡˈeɪʃən,
+DUMMY1/LJ046-0033.wav|ðɪ ˈædɪkwəsi ʌv ɛɡzˈɪstɪŋ pɹəsˈiːdʒɚz kæn fˈɛɹli biː ɐsˈɛst ˈoʊnli ˈæftɚ fˈʊl kənsˌɪdɚɹˈeɪʃən ʌvðə dˈɪfɪkˌʌlti ʌvðə pɹətˈɛktɪv ɐsˈaɪnmənt,
+DUMMY1/LJ037-0061.wav|ænd hˈævɪŋ, kwˈoʊt, sˈʌmwʌt bˈʊʃi, ˈɛnd kwˈoʊt, hˈɛɹ.
+DUMMY1/LJ032-0025.wav|ðɪ ˈɑːfɪsɚz ʌv klˈaɪnz dɪskˈʌvɚd ðˌæɾɚ ɹˈaɪfəl bˈɛɹɪŋ sˈiəɹɪəl nˈʌmbɚ sˈiː tˈuː sˈɛvən sˈɪks sˈɪks hɐdbɪn ʃˈɪpt tə wˈʌn ˈeɪ. hˈaɪdəl,
+DUMMY1/LJ047-0197.wav|ɪn vjˈuː ʌv ˈɔːl ðɪ ˌɪnfɚmˈeɪʃən kənsˈɜːnɪŋ ˈɑːswəld ɪn ɪts fˈaɪlz, ʃˌʊdəv ɐlˈɜːɾᵻd ðə sˈiːkɹət sˈɜːvɪs tʊ ˈɑːswəldz pɹˈɛzəns ɪn dˈæləs
+DUMMY1/LJ018-0130.wav|ænd stˈoʊl pˈeɪpɚɹ ˌɑːn ɐ mˈʌtʃ lˈɑːɹdʒɚ skˈeɪl ðɐn bɹˈaʊn.
+DUMMY1/LJ005-0265.wav|ɪt wʌz ɹˌɛkəmˈɛndᵻd ðætðə dˈaɪətɚɹiz ʃˌʊd biː səbmˈɪɾᵻd ænd ɐpɹˈuːvd lˈaɪk ðə ɹˈuːlz; ðæt kənvˈɪktᵻd pɹˈɪzənɚz ʃˌʊd nˌɑːt ɹɪsˈiːv ˌɛni fˈuːd bˌʌt ðə dʒˈeɪl ɐlˈaʊəns;
+DUMMY1/LJ044-0105.wav|hiː pɹɪzˈɛntᵻd ˈɑːɹnoʊld dʒˈɑːnsən, ɡˈʌs hˈɔːl,
+DUMMY1/LJ015-0043.wav|ðɪs wɛnt ˌɑːn fɔːɹ sˌʌm tˈaɪm, ænd mˌaɪt nˈɛvɚ hɐvbɪn dɪskˈʌvɚd hˌæd sʌm ɡˈʊd stɹˈoʊk ʌv lˈʌk pɹəvˈaɪdᵻd ˌɛni ʌvðə pˈɑːɹtnɚz
+DUMMY1/LJ030-0125.wav|ˌɑːn sˈɛvɹəl əkˈeɪʒənz wɛn ðə vˈaɪs pɹˈɛzɪdənts kˈɑːɹ wʌz slˈoʊd dˌaʊn baɪ ðə θɹˈɔŋ, spˈɛʃəl ˈeɪdʒənt jˈʌŋblʌd stˈɛpt ˈaʊt tə hˈoʊld ðə kɹˈaʊd bˈæk.
+DUMMY1/LJ043-0140.wav|hiː ˈɑːlsoʊ stˈʌdɪd dˈæləs bˈʌs skˈɛdʒuːlz tə pɹɪpˈɛɹ fɔːɹ hɪz lˈeɪɾɚ jˈuːs ʌv bˈʌsᵻz tə tɹˈævəl tʊ ænd fɹʌm dʒˈɛnɚɹəl wˈɔːkɚz hˈaʊs.
+DUMMY1/LJ002-0220.wav|ɪn kˈɑːnsɪkwəns ʌv ðiːz dɪsklˈoʊʒɚz, bˈoʊθ bæmbɹˈɪdʒ ænd hˈʌɡɪn, hɪz pɹˈɛdᵻsˌɛsɚɹ ɪnðɪ ˈɑːfɪs, wɜː kəmˈɪɾᵻd tə nˈuːɡeɪt,
+DUMMY1/LJ034-0117.wav|æt wˈʌn:twˈɛntinˈaɪn pˈiː.ˈɛm. ðə pəlˈiːs ɹˈeɪdɪˌoʊ ɹɪpˈoːɹɾᵻd
+DUMMY1/LJ018-0276.wav|ðə fˈɜːst plˈɑːt wʌz ɐɡˈɛnst mˈɪstɚ hˈæɹi ɪmˈænuːl, bˌʌt hiː ɛskˈeɪpt, ænd ðɪ ɐtˈɛmpt wʌz mˌeɪd əpˌɑːn lˈaʊdɑːn ænd ɹˈaɪdɚ.
+DUMMY1/LJ004-0077.wav|nˈɔːɹ hɐz hiː ɐ ɹˈaɪt tə pˈɔɪzən ɔːɹ stˈɑːɹv hɪz fˈɛloʊkɹˈiːtʃɚz."
+DUMMY1/LJ042-0194.wav|ðeɪ ʃˌʊd nˌɑːt biː kənfjˈuːzd wɪð slˈoʊnəs, ˌɪndᵻsˈɪʒən ɔːɹ fˈɪɹ. ˈoʊnli ðɪ ˌɪntəlˈɛktʃuːəli fˈɪɹləs kʊd ˈiːvən biː ɹɪmˈoʊtli ɐtɹˈæktᵻd tʊ ˌaʊɚ dˈɑːktɹɪn,
+DUMMY1/LJ029-0114.wav|ðə ɹˈaʊt tʃˈoʊzən fɹʌmðɪ ˈɛɹpoːɹt tə mˈeɪn stɹˈiːt wʌzðə nˈoːɹməl wˌʌn, ɛksˈɛpt wˌɛɹ hˈɑːɹwʊd stɹˈiːt wʌz sɪlˈɛktᵻd æz ðə mˈiːnz ʌv ˈæksɛs tə mˈeɪn stɹˈiːt
+DUMMY1/LJ014-0194.wav|ðə pəlˈiːsmɛn wɜː nˈaʊ ɪn pəzˈɛʃən;
+DUMMY1/LJ032-0027.wav|ɐkˈoːɹdɪŋ tʊ ɪts mˈaɪkɹoʊfˌɪlm ɹˈɛkɚdz, klˈaɪnz ɹɪsˈiːvd ɐn ˈɔːɹdɚ fɚɹɚ ɹˈaɪfəl ˌɑːn mˈɑːɹtʃ θɜːtˈiːn, naɪntˈiːn sˈɪkstiθɹˈiː,
+DUMMY1/LJ048-0289.wav|haʊˈɛvɚ, ðɛɹ ɪz nˈoʊ ˈɛvɪdəns ðæt ðiːz mˈɛn fˈeɪld tə tˈeɪk ˌɛni ˈækʃən ɪn dˈæləs wɪðˌɪn ðɛɹ pˈaʊɚ ðæt wʊdhɐv ɐvˈɜːɾᵻd ðə tɹˈædʒədi.
+DUMMY1/LJ043-0188.wav|ðæt hiː wʌzðə lˈiːdɚɹ əvə fˈæʃɪst ˌɔːɹɡɐnaɪzˈeɪʃən, ænd wɛn ˈaɪ sˈɛd ðæt ˈiːvən ðˌoʊ ˈɔːl ʌv ðæt mˌaɪt biː tɹˈuː, dʒˈʌst ðə sˈeɪm hiː hɐd nˈoʊ ɹˈaɪt tə tˈeɪk hɪz lˈaɪf,
+DUMMY1/LJ011-0118.wav|ɪn eɪtˈiːn twˈɛntinˈaɪn ðə ɡˈæloʊz klˈeɪmd tˈuː mˈoːɹ vˈɪktᵻmz fɔːɹ ðɪs əfˈɛns.
+DUMMY1/LJ040-0201.wav|ˈæftɚ hɜːɹ ˈɪntɚvjˌuː wɪð mɪsˈɛs ˈɑːswəld,
+DUMMY1/LJ033-0056.wav|wˌaɪl ðə ɹˈaɪfəl mˌeɪhɐv ɔːlɹˌɛdi bˌɪn dˌɪsɐsˈɛmbəld wɛn ˈɑːswəld ɐɹˈaɪvd hˈoʊm ˌɑːn θˈɜːzdeɪ, hiː hɐd ˈæmpəl tˈaɪm ðæt ˈiːvnɪŋ tə dˌɪsɐsˈɛmbəl ðə ɹˈaɪfəl
+DUMMY1/LJ047-0073.wav|hˈɑːsti kənsˈɪdɚd ðɪ ˌɪnfɚmˈeɪʃən tə bˈiː, kwˈoʊt, stˈeɪl, ʌnkwˈoʊt, baɪ ðæt tˈaɪm, ænd dɪdnˌɑːt ɐtˈɛmpt tə vˈɛɹɪfˌaɪ ˈɑːswəldz ɹɪpˈoːɹɾᵻd stˈeɪtmənt.
+DUMMY1/LJ001-0153.wav|ˈoʊnli nˈɑːmɪnəli sˈoʊ, haʊˈɛvɚ, ɪn mˈɛni kˈeɪsᵻz, sˈɪns wɛn hiː jˈuːzᵻz ɐ hˈɛdlaɪn hiː kˈaʊnts ðæt ˈɪn,
+DUMMY1/LJ007-0158.wav|ɔːɹ ˌɛni kˈaɪnd ʌv mˈɔːɹəl ɪmpɹˈuːvmənt wʌz ɪmpˈɑːsəbəl; ðə pɹˈɪzənɚz kɚɹˈɪɹ wʌz ɪnˈɛvɪɾəbli dˈaʊnwɚd, tˈɪl hiː stɹˈʌk ðə lˈoʊəst dˈɛpθs.
+DUMMY1/LJ028-0502.wav|ðɪ ˈɪʃtɚ ɡˈeɪtweɪ lˈiːdɪŋ tə ðə pˈælɪs wʌz ɛnkˈeɪst wɪð bjˈuːɾɪfəl blˈuː ɡlˈeɪzd bɹˈɪks,
+DUMMY1/LJ028-0226.wav|ðˌoʊ hˈiəɹoʊdˌɑːɾəs ɹˈoʊt nˌɪɹli ɐ hˈʌndɹəd jˈɪɹz ˈæftɚ bˈæbɪlən fˈɛl, hɪz stˈoːɹi sˈiːmz tə bˈɛɹ ðə stˈæmp ʌv tɹˈuːθ.
+DUMMY1/LJ010-0038.wav|æz ðɛɹ hɐdbɪn bɪfˈoːɹ; æz ɪnðə jˈɪɹ eɪtˈiːn fˈɔːɹtinˈaɪn, ɐ jˈɪɹ mˈɛmɚɹəbəl fɚðə ɹˈʌʃ mˈɜːdɚz æt nˈɔːɹɪtʃ,
+DUMMY1/LJ019-0241.wav|bˌʌt ɪnðɪ ˈɪntɚvəl vˈɛɹi kˌɑːmpɹɪhˈɛnsɪv ænd, ˈaɪ θˈɪŋk ɪt mˈʌst biː ɐdmˈɪɾᵻd, sˈæluːtˌɛɹi tʃˈeɪndʒᵻz wɜː səksˈɛsɪvli ˌɪntɹədˈuːst ˌɪntʊ ðə mˈænɪdʒmənt ʌv pɹˈɪzənz.
+DUMMY1/LJ001-0094.wav|wɜːɹ ɪndˈuːst tə kˈʌt pˈʌntʃᵻz fɚɹə sˈɪɹiz ʌv "ˈoʊld stˈaɪl" lˈɛɾɚz.
+DUMMY1/LJ001-0015.wav|ðə fˈɔːɹmz ʌv pɹˈɪntᵻd lˈɛɾɚz ʃˌʊd biː bjˈuːɾɪfəl, ænd ðæt ðɛɹ ɐɹˈeɪndʒmənt ɑːnðə pˈeɪdʒ ʃˌʊd biː ɹˈiːzənəbəl ænd ɐ hˈɛlp tə ðə ʃˈeɪplinəs ʌvðə lˈɛɾɚz ðɛmsˈɛlvz.
+DUMMY1/LJ047-0015.wav|fɹʌm dɪfˈɛkʃən tə ɹɪtˈɜːn tə fˈɔːɹt wˈɜːθ.
+DUMMY1/LJ044-0139.wav|sˈɪns ðɛɹwˌʌz nˈoʊ bˈækɡɹaʊnd tə ðə nˈuː ˈɔːɹliənz ˌɛfpˌiːsˌiːsˈiː, kwˈoʊt, ˌɔːɹɡɐnaɪzˈeɪʃən, ˈɛnd kwˈoʊt, wˌɪtʃ kənsˈɪstᵻd sˈoʊlli ʌv ˈɑːswəld.
+DUMMY1/LJ050-0031.wav|ðætðə sˈiːkɹət sˈɜːvɪs kˈɑːnʃəsli sˈɛt ɐbˌaʊt ðə tˈæsk ʌv ˈɪnkəlkˌeɪɾɪŋ ænd meɪntˈeɪnɪŋ ðə hˈaɪəst stˈændɚd ʌv ˈɛksələns ænd ɛspɹˈɪt, fɔːɹ ˈɔːl ʌv ɪts pˌɜːsənˈɛl.
+DUMMY1/LJ050-0235.wav|ɪt hɐz ˈɑːlsoʊ jˈuːzd ˈʌðɚ fˈɛdɚɹəl lˈɔː ɛnfˈoːɹsmənt ˈeɪdʒənts dˈʊɹɪŋ pɹˌɛzɪdˈɛnʃəl vˈɪzɪts tə sˈɪɾiz ɪn wˌɪtʃ sˈʌtʃ ˈeɪdʒənts ɑːɹ stˈeɪʃənd.
+DUMMY1/LJ050-0137.wav|ˌɛfbˌiːˈaɪ, ænd ðə sˈiːkɹət sˈɜːvɪs.
+DUMMY1/LJ031-0109.wav|æt wˈʌn:θˈɜːɾifˈaɪv pˈiː.ˈɛm., ˈæftɚ ɡˈʌvɚnɚ kənˈæli hɐdbɪn mˈuːvd tə ðɪ ˈɑːpɚɹˌeɪɾɪŋ ɹˈuːm, dˈɑːktɚ ʃˈɔː stˈɑːɹɾᵻd ðə fˈɜːst ˌɑːpɚɹˈeɪʃən
+DUMMY1/LJ031-0041.wav|hiː nˈoʊɾᵻd ðætðə pɹˈɛzɪdənt wʌz blˈuːwˈaɪt ɔːɹ ˈæʃən ɪn kˈʌlɚ; hɐd slˈoʊ, spæzmˈɑːdɪk, ˈæɡənəl ɹˌɛspᵻɹˈeɪʃən wɪðˌaʊt ˌɛni koʊˈɔːɹdᵻnˈeɪʃən;
+DUMMY1/LJ021-0139.wav|ðɛɹ ʃˌʊd biː æt lˈiːst ɐ fˈʊl ænd fˈɛɹ tɹˈaɪəl ɡˈɪvən tə ðiːz mˈiːnz ʌv ˈɛndɪŋ ɪndˈʌstɹɪəl wˈɔːɹfɛɹ;
+DUMMY1/LJ029-0004.wav|ðə nˈæɹətˌɪv ʌv ðiːz ɪvˈɛnts ɪz bˈeɪst lˈɑːɹdʒli ɑːnðə ɹɪkəlˈɛkʃənz ʌvðə pɑːɹtˈɪsɪpənts,
+DUMMY1/LJ023-0122.wav|ɪt wʌz sˈɛd ɪn lˈæst jˈɪɹz dˌɛməkɹˈæɾɪk plˈætfɔːɹm,
+DUMMY1/LJ005-0264.wav|ɪnspˈɛktɚz ʌv pɹˈɪzənz ʃˌʊd biː ɐpˈɔɪntᵻd, hˌuː ʃˌʊd vˈɪzɪt ˈɔːl ðə pɹˈɪzənz fɹʌm tˈaɪm tə tˈaɪm ænd ɹɪpˈoːɹt tə ðə sˈɛkɹətɹi ʌv stˈeɪt.
+DUMMY1/LJ002-0105.wav|ænd bɪjˌɑːnd ɪt wʌzɐ ɹˈuːm kˈɔːld ðə "wˈaɪn ɹˈuːm," bɪkˈʌz fˈɔːɹmɚli jˈuːzd fɚðə sˈeɪl ʌv wˈaɪn, bˈʌt
+DUMMY1/LJ017-0035.wav|ɪnðɪ ˈɪntɹəsts ænd fɚðə dˈuː pɹətˈɛkʃən ʌvðə pˈʌblɪk, ðætðə fˈʊləst ænd fˈɛɹəst ˈɪnkwɚɹi ʃˌʊd biː mˈeɪd,
+DUMMY1/LJ048-0252.wav|θɹˈiː ʌv ðiːz ˈeɪdʒənts ˈɑːkjʊpˌaɪd pəzˈɪʃənz ɑːnðə ɹˈʌnɪŋ bˈoːɹdz ʌvðə kˈɑːɹ, ænd ðə fˈoːɹθ wʌz sˈiːɾᵻd ɪnðə kˈɑːɹ.
+DUMMY1/LJ013-0109.wav|ðə pɹˈoʊsiːdz ʌvðə ɹˈɑːbɚɹi wɜː lˈɑːdʒd ɪn ɐ bˈɔstən bˈæŋk,
+DUMMY1/LJ039-0139.wav|ˈɑːswəld əbtˈeɪnd ɐ hˈʌntɪŋ lˈaɪsəns, dʒˈɔɪnd ɐ hˈʌntɪŋ klˈʌb ænd wɛnt hˈʌntɪŋ ɐbˌaʊt sˈɪks tˈaɪmz, æz dɪskˈʌst mˈoːɹ fˈʊli ɪn tʃˈæptɚ sˈɪks.
+DUMMY1/LJ044-0047.wav|ðæt ˈɛnɪwˌʌn ˈɛvɚɹ ɐtˈækt ˌɛni stɹˈiːt dˌɛmənstɹˈeɪʃən ɪn wˌɪtʃ ˈɑːswəld wʌz ɪnvˈɑːlvd, ɛksˈɛpt fɚðə bɹˈɪŋɡaɪɚɹ ˈɪnsɪdənt mˈɛnʃənd əbˈʌv,
+DUMMY1/LJ016-0417.wav|kˈæθɹɪn wˈɪlsən, ðə pˈɔɪzənɚ, wʌz ɹɪsˈɜːvd ænd ɹˈɛɾɪsənt tə ðə lˈæst, ɛkspɹˈɛsɪŋ nˈoʊ kəntɹˈɪʃən, bˌʌt ˈɑːlsoʊ nˈoʊ fˈɪɹ
+DUMMY1/LJ045-0178.wav|hiː lˈɛft hɪz wˈɛdɪŋ ɹˈɪŋ ɪn ɐ kˈʌp ɑːnðə dɹˈɛsɚɹ ɪn hɪz ɹˈuːm. hiː ˈɑːlsoʊ lˈɛft wˈʌn hˈʌndɹəd sˈɛvənti dˈɑːlɚz ɪn ɐ wˈɑːlɪt ɪn wˈʌn ʌvðə dɹˈɛsɚ dɹˈɔːɹz.
+DUMMY1/LJ009-0172.wav|wˌaɪl ɪn lˈʌndən, fɔːɹ ˈɪnstəns, ɪn eɪtˈiːn twˈɛntinˈaɪn, twˈɛntifˈoːɹ pˈɜːsənz hɐdbɪn ˈɛksɪkjˌuːɾᵻd fɔːɹ kɹˈaɪmz ˈʌðɚ ðɐn mˈɜːdɚ,
+DUMMY1/LJ049-0202.wav|ˈɪnsɪdənt tʊ ɪts ɹɪspˌɑːnsəbˈɪlɪɾiz.
+DUMMY1/LJ032-0103.wav|ðə nˈeɪm "hˈaɪdəl" wʌz stˈæmpt ˈɑːn sˌʌm ʌvðə "tʃˈæptɚz" pɹˈɪntᵻd lˈɪɾɚɹətʃɚ ænd ɑːnðə mˈɛmbɚʃˌɪp ˌæplɪkˈeɪʃən blˈæŋks.
+DUMMY1/LJ013-0091.wav|ænd ˈɛldɚ hædtə biː ɐsˈɪstᵻd baɪ tˈuː bˈæŋk pˈoːɹɾɚz, hˌuː kˈæɹɪd ɪt fɔːɹ hˌɪm tʊ ɐ kˈæɹɪdʒ wˈeɪɾɪŋ nˌɪɹ ðə mˈænʃən hˈaʊs.
+DUMMY1/LJ037-0208.wav|naɪntˈiːn dˈɑːlɚz, nˈaɪntifˈaɪv sˈɛnts, plˈʌs wˈʌn dˈɑːlɚ, twˈɛntisˈɛvən sˈɛnts ʃˈɪpɪŋ tʃˈɑːɹdʒ, hɐdbɪn kəlˈɛktᵻd fɹʌmðə kənsˈaɪniː, hˈaɪdəl.
+DUMMY1/LJ014-0128.wav|hɜː hˈɛɹ wʌz dɹˈɛst ɪn lˈɑːŋ kɹˈeɪp bˈændz. ʃiː hɐd lˈeɪs ɹˈʌfəlz æt hɜː ɹˈɪst, ænd wˈoːɹ pɹˈɪmɹoʊzkˈʌlɚd kˈɪd ɡlˈʌvz.
+DUMMY1/LJ015-0007.wav|ðɪs ɐfˈɛktᵻd kˈoʊlz kɹˈɛdɪt, ænd ˈʌɡli ɹɪpˈoːɹts wɜːɹ ɪn sˌɜːkjʊlˈeɪʃən tʃˈɑːɹdʒɪŋ hˌɪm wɪððɪ ˈɪʃuː ʌv sˈɪmjʊlˌeɪɾᵻd wˈɔːɹənts.
+DUMMY1/LJ036-0169.wav|hiː wʊdhɐv ɹˈiːtʃt hɪz dˌɛstɪnˈeɪʃən æt ɐpɹˈɑːksɪmətli twˈɛlv:fˈɪftifˈoːɹ pˈiː.ˈɛm.
+DUMMY1/LJ021-0040.wav|ðə sˈɛkənd stˈɛp wiː hæv tˈeɪkən ɪnðə ɹˌɛstɚɹˈeɪʃən ʌv nˈoːɹməl bˈɪznəs ˈɛntɚpɹˌaɪz
+DUMMY1/LJ015-0036.wav|ðə bˈæŋk wʌz ɔːlɹˌɛdi ɪnsˈɑːlvənt,
+DUMMY1/LJ034-0041.wav|ɑːlðˈoʊ bjˈʊɹɹoʊ ɛkspˈɛɹɪmənts hɐd ʃˈoʊn ðæt twˈɛntifˈoːɹ ˈaɪʊɹz wʌzɐ lˈaɪkli mˈæksɪməm tˈaɪm, lætˈoʊnə stˈeɪɾᵻd
+DUMMY1/LJ009-0192.wav|ðə daɪsˈɛkʃən ʌv ˈɛksɪkjˌuːɾᵻd kɹˈɪmɪnəlz wʌz ɐbˈɑːlɪʃt sˈuːn ˈæftɚ ðə dɪskˈʌvɚɹi ʌvðə kɹˈaɪm ʌv bˈɜːkɪŋ,
+DUMMY1/LJ037-0248.wav|ðɪ ˈaɪwɪtnəsᵻz vˈɛɹi ɪn ðɛɹ aɪdˈɛntɪfɪkˈeɪʃən ʌvðə dʒˈækɪt.
+DUMMY1/LJ015-0289.wav|æz ˈiːtʃ tɹænsˈækʃən wʌz kˈæɹɪd ˈaʊt fɹʌm ɐ dˈɪfɹənt ɐdɹˈɛs, ænd ɐ dˈɪfɹənt mˈɛsɪndʒɚɹ ˈɔːlweɪz ɛmplˈɔɪd,
+DUMMY1/LJ005-0072.wav|ˈæftɚɹ ɐ fjˈuː jˈɪɹz ʌv ˈæktɪv ɛɡzˈɜːʃən ðə səsˈaɪəɾi wʌz ɹɪwˈɔːɹdᵻd baɪ fɹˈɛʃ lˌɛdʒɪslˈeɪʃən.
+DUMMY1/LJ023-0047.wav|ðə θɹˈiː hˈɔːɹsᵻz ɑːɹ, ʌv kˈoːɹs, ðə θɹˈiː bɹˈæntʃᵻz ʌv ɡˈʌvɚnmənt ðə kˈɑːnɡɹəs, ðɪ ɛɡzˈɛkjuːtˌɪv ænd ðə kˈoːɹts.
+DUMMY1/LJ009-0126.wav|hˈɑːɹdli ˌɛni wˌʌn.
+DUMMY1/LJ034-0097.wav|ðə wˈɪndoʊ wʌz ɐpɹˈɑːksɪmətli wˈʌn hˈʌndɹəd twˈɛnti fˈiːt ɐwˈeɪ.
+DUMMY1/LJ028-0462.wav|ðeɪ wɜː lˈeɪd ɪn bᵻtˈuːmɛn.
+DUMMY1/LJ046-0055.wav|ɪt ɪz nˈaʊ pˈɑːsəbəl fɔːɹ pɹˈɛzɪdənts tə tɹˈævəl ðə lˈɛŋθ ænd bɹˈɛdθ əvə lˈænd fˈɑːɹ lˈɑːɹdʒɚ ðɐn ðə juːnˈaɪɾᵻd stˈeɪts
+DUMMY1/LJ019-0371.wav|jˈɛt ðə lˈɔː wʌz sˈɛldəm ɪf ˈɛvɚɹ ɛnfˈoːɹst.
+DUMMY1/LJ039-0207.wav|ɑːlðˈoʊ ˈɔːl ʌvðə ʃˈɑːts wɜːɹ ɐ fjˈuː ˈɪntʃᵻz hˈaɪ ænd tə ðə ɹˈaɪt ʌvðə tˈɑːɹɡɪt,
+DUMMY1/LJ002-0174.wav|mˈɪstɚ bˈʌkstənz fɹˈɛndz ɐtwˈʌns pˈeɪd ðə fˈɔːɹɾi ʃˈɪlɪŋz, ænd ðə bˈɔɪ wʌz ɹɪlˈiːsd.
+DUMMY1/LJ016-0233.wav|ɪn hɪz ˈoʊn pɹəfˈɛʃən
+DUMMY1/LJ026-0108.wav|ɪt ɪz klˈɪɹ ðæt ðɛɹˌɑːɹ ˈʌpwɚd ænd dˈaʊnwɚd kˈɜːɹənts ʌv wˈɔːɾɚ kəntˈeɪnɪŋ fˈuːd kˈɑːmpɚɹəbəl tə blˈʌd əvən ˈænɪməl,
+DUMMY1/LJ038-0035.wav|ˈɑːswəld ɹˈoʊz fɹʌm hɪz sˈiːt, bɹˈɪŋɪŋ ˌʌp bˈoʊθ hˈændz.
+DUMMY1/LJ026-0148.wav|wˈɔːɾɚ wˌɪtʃ ɪz lˈɔst baɪ ɪvˌæpɚɹˈeɪʃən, ɪspˈɛʃəli fɹʌmðə lˈiːf sˈɜːfɪs θɹuː ðə stˈoʊməɾə;
+DUMMY1/LJ001-0186.wav|ðə pəzˈɪʃən ʌv ˌaʊɚ səsˈaɪəɾi ðˌæɾə wˈɜːk ʌv juːtˈɪlɪɾi mˌaɪt biː ˈɑːlsoʊ ɐ wˈɜːk ʌv ˈɑːɹt, ɪf wiː kˈɛɹd tə mˌeɪk ɪt sˈoʊ.
+DUMMY1/LJ016-0264.wav|ðɪ ˈʌptɜːnd fˈeɪsᵻz ʌvðɪ ˈiːɡɚ spɛktˈeɪɾɚz ɹɪsˈɛmbəld ðoʊz ʌvðə ɡˈɑːdz æt dɹˈʌɹi lˈeɪn ˌɑːn bˈɑːksɪŋ nˈaɪt;
+DUMMY1/LJ009-0041.wav|ðɪ ˈɑːkjʊpənts ʌv ðɪs tˈɛɹəbəl blˈæk pjˈuː wɜː ðə lˈæst ˈɔːlweɪz tʊ ˈɛntɚ ðə tʃˈæpəl.
+DUMMY1/LJ010-0297.wav|bˌʌt ðɛɹwˌɜːɹ ˈʌðɚ noʊtˈoːɹɪəs kˈeɪsᵻz ʌv fˈɔːɹdʒɚɹi.
+DUMMY1/LJ040-0018.wav|ðə kəmˈɪʃən ɪz nˌɑːt ˈeɪbəl tə ɹˈiːtʃ ˌɛni dˈɛfɪnət kənklˈuːʒənz æz tə wˈɛðɚ ɔːɹ nˌɑːt hiː wʌz, kwˈoʊt, sˈeɪn, ʌnkwˈoʊt, ˌʌndɚ pɹɪvˈeɪlɪŋ lˈiːɡəl stˈændɚdz.
+DUMMY1/LJ005-0253.wav|"tʊ ɪnkwˈaɪɚɹ ˌɪntʊ ænd ɹɪpˈoːɹt əpˌɑːn ðə sˈɛvɹəl dʒˈeɪlz ænd hˈaʊzɪz ʌv kɚɹˈɛkʃən ɪnðə kˈaʊntɪz, sˈɪɾiz, ænd kˈɔːɹpɚɹət tˈaʊnz wɪðˌɪn ˈɪŋɡlənd ænd wˈeɪlz
+DUMMY1/LJ027-0176.wav|fˈɪʃᵻz fˈɜːst ɐpˈɪɹd ɪnðə dɛvˈoʊniən ænd ˌʌpɚ sɪlˈʊɹiən ɪn vˈɛɹi ɹɛptˈɪliən ɔːɹ ɹˈæðɚɹ æmfˈɪbiən fˈɔːɹmz.
+DUMMY1/LJ034-0035.wav|ðə pəzˈɪʃən ʌv ðɪs pˈɑːmpɹɪnt ɑːnðə kˈɑːɹtən wʌz pˈæɹəlˌɛl wɪððə lˈɑːŋ ˈæksɪs ʌvðə bˈɑːks, ænd æt ɹˈaɪt ˈæŋɡəlz wɪððə ʃˈɔːɹt ˈæksɪs;
+DUMMY1/LJ016-0054.wav|bˌʌt hiː dɪdnˌɑːt lˈaɪk ðə ɹˈɪsk ʌv ˈɛntɚɹɪŋ ɐ ɹˈuːm baɪ ðə fˈaɪɚpleɪs, ænd ðə tʃˈænsᵻz ʌv dɪtˈɛkʃən ɪt ˈɑːfɚd.
+DUMMY1/LJ018-0262.wav|ɹˈuːpɛl ɹɪsˈiːvd ðɪ ɐnˈaʊnsmənt wɪð ɐ tʃˈɪɹfəl kˈaʊntənəns,
+DUMMY1/LJ044-0237.wav|wɪð θɜːtˈiːn dˈɑːlɚz, ˈeɪɾisˈɛvən sˈɛnts wɛn kənsˈɪdɚɹəbli ɡɹˈeɪɾɚ ɹɪsˈoːɹsᵻz wɜːɹ ɐvˈeɪləbəl tə hˌɪm.
+DUMMY1/LJ034-0166.wav|tˈuː ˈʌðɚ wˈɪtnəsᵻz wɜːɹ ˈeɪbəl tʊ ˈɑːfɚ pˈɑːɹʃəl dɪskɹˈɪpʃənz əvə mˈæn ðeɪ sˈɔː ɪnðə sˈaʊθiːst kˈɔːɹnɚ wˈɪndoʊ
+DUMMY1/LJ016-0238.wav|"dʒˈʌst tə stˈɛdi ðɛɹ lˈɛɡz ɐ lˈɪɾəl;" ɪn ˈʌðɚ wˈɜːdz, tʊ ˈæd hɪz wˈeɪt tə ðæt ʌvðə hˈæŋɪŋ bˈɑːdɪz.
+DUMMY1/LJ042-0198.wav|ðə dɪskˈʌʃən əbˌʌv hɐz ɔːlɹˌɛdi sˈɛt fˈɔːɹθ ɛɡzˈæmpəlz ʌv hɪz ɛkspɹˈɛʃən ʌv hˈeɪtɹᵻd fɚðə juːnˈaɪɾᵻd stˈeɪts.
+DUMMY1/LJ031-0189.wav|æt tˈuː:θˈɜːɾiˈeɪt pˈiː.ˈɛm., ˈiːstɚn stˈændɚd tˈaɪm, lˈɪndən bˈeɪnz dʒˈɑːnsən tˈʊk ðɪ ˈoʊθ ʌv ˈɑːfɪs æz ðə θˈɜːɾisˈɪksθ pɹˈɛzɪdənt ʌvðə juːnˈaɪɾᵻd stˈeɪts.
+DUMMY1/LJ050-0084.wav|ˈɔːɹ, kwˈoʊt, ˈʌðɚ hˈaɪ ɡˈʌvɚnmənt əfˈɪʃəlz ɪnðə nˈeɪtʃɚɹ əvə kəmplˈeɪnt kˈʌpəld wɪð ɐn ɛkspɹˈɛst ɔːɹ ɪmplˈaɪd dɪtˌɜːmᵻnˈeɪʃən tə jˈuːz ɐ mˈiːnz,
+DUMMY1/LJ044-0158.wav|æz fɔːɹ maɪ ɹɪtˈɜːn ˈɛntɹəns vˈiːzə plˈiːz kənsˈɪdɚɹ ɪt sˈɛpɹətli. ˈɛnd kwˈoʊt.
+DUMMY1/LJ045-0082.wav|ɪt ɐpˈɪɹz ðæt mɚɹˈiːnə ˈɑːswəld ˈɑːlsoʊ kəmplˈeɪnd ðæt hɜː hˈʌsbənd wʌz nˌɑːt ˈeɪbəl tə pɹəvˈaɪd mˈoːɹ mətˈiəɹɪəl θˈɪŋz fɔːɹ hɜː.
+DUMMY1/LJ045-0190.wav|ɐpˈɪɹd ɪnðə dˈæləs tˈaɪmz hˈɛɹəld ˌɑːn noʊvˈɛmbɚ fɪftˈiːn, naɪntˈiːn sˈɪkstiθɹˈiː.
+DUMMY1/LJ035-0155.wav|ðɪ ˈoʊnli ˈɛɡzɪt fɹʌmðɪ ˈɑːfɪs ɪnðə dɚɹˈɛkʃən ˈɑːswəld wʌz mˈuːvɪŋ wʌz θɹuː ðə dˈoːɹ tə ðə fɹˈʌnt stˈɛɹweɪ.
+DUMMY1/LJ044-0004.wav|pəlˈɪɾɪkəl æktˈɪvɪɾiz
+DUMMY1/LJ046-0016.wav|ðə kəmˈɪʃən həznɑːt ˌʌndɚtˈeɪkən ɐ kˌɑːmpɹɪhˈɛnsɪv ɛɡzˌæmᵻnˈeɪʃən ʌv ˈɔːl fˈæsɛts ʌv ðɪs sˈʌbdʒɛkt;
+DUMMY1/LJ019-0368.wav|ðə lˈæɾɚ tˈuː wʌz təbi lˈeɪd bɪfˌoːɹ ðə hˈaʊs ʌv kˈɑːmənz.
+DUMMY1/LJ010-0062.wav|bˌʌt ðeɪ pɹəsˈiːdᵻd ɪn ˈɔːl sˈiəɹɪəsnəs, ænd wʊdhɐv ʃɹˈʌŋk fɹʌm nˈoʊ ˈaʊtɹeɪdʒ ɔːɹ ɐtɹˈɑːsɪɾi ɪn fˈɜːθɚɹəns ʌv ðɛɹ fˈuːlhɑːɹdi ˈɛntɚpɹˌaɪz.
+DUMMY1/LJ033-0159.wav|ɪt wʌz fɹʌm ˈɑːswəldz ɹˈaɪt hˈænd, ɪn wˌɪtʃ hiː kˈæɹɪd ðə lˈɑːŋ pˈækɪdʒ æz hiː wˈɔːkt fɹʌm fɹˈeɪzɪɚz kˈɑːɹ tə ðə bˈɪldɪŋ.
+DUMMY1/LJ002-0171.wav|ðə bˈɔɪ dᵻklˈɛɹd hiː sˈɔː nˈoʊwˈʌn, ænd ɐkˈoːɹdɪŋli pˈæst θɹuː wɪðˌaʊt pˈeɪɪŋ ðə tˈoʊl əvə pˈɛni.
+DUMMY1/LJ002-0298.wav|ɪn hɪz ˈɛvɪdəns ɪn eɪtˈiːn foːɹtˈiːn, sˈɛd ɪt wʌz mˈoːɹ,
+DUMMY1/LJ012-0219.wav|ænd ɪn wˈʌn kˈɔːɹnɚ, æt sˌʌm dˈɛpθ, ɐ bˈʌndəl ʌv klˈoʊðz wɜːɹ ʌnˈɜːθd, wˈɪtʃ, wɪð ɐ hˈɛɹi kˈæp,
+DUMMY1/LJ017-0190.wav|ˈæftɚ ðɪs kˈeɪm ðə tʃˈɑːɹdʒ ʌv ɐdmˈɪnɪstɚɹɪŋ ˈɔɪl ʌv vˈɪtɹiːəl, wˌɪtʃ fˈeɪld, æz hɐzbɪn dɪskɹˈaɪbd.
+DUMMY1/LJ019-0179.wav|ðˈɪs, wɪð ɐ skˈiːm fɔːɹ lˈɪmɪɾɪŋ ðə dʒˈeɪl tʊ ʌntɹˈaɪd pɹˈɪzənɚz, hɐdbɪn ˈɜːdʒəntli ɹˌɛkəmˈɛndᵻd baɪ lˈɔːɹd dʒˈɑːn ɹˈʌsəl ɪn eɪtˈiːn θˈɜːɾi.
+DUMMY1/LJ050-0188.wav|ˈiːtʃ pɐtɹˈoʊlmən mˌaɪt biː ɡˈɪvən ɐ pɹɪpˈɛɹd bˈʊklət ʌv ɪnstɹˈʌkʃənz ɛksplˈeɪnɪŋ wˌʌt ɪz ɛkspˈɛktᵻd ʌv hˌɪm. ðə sˈiːkɹət sˈɜːvɪs hɐz ɛkspɹˈɛst kənsˈɜːn
+DUMMY1/LJ006-0043.wav|ðə dɪsɡɹˈeɪsfəl ˌoʊvɚkɹˈaʊdɪŋ hɐdbɪn pˈɑːɹʃəli ˈɛndᵻd, bˌʌt ðə sˈeɪm ˈiːvəlz ʌv ˌɪndɪskɹˈɪmᵻnət ɐsˈoʊsɪˈeɪʃən wɜː stˈɪl pɹɪzˈɛnt; ðɛɹwˌʌz ðɪ ˈoʊld nɪɡlˈɛkt ʌv dˈiːsənsi,
+DUMMY1/LJ029-0060.wav|ɐ nˈʌmbɚɹ ʌv pˈiːpəl hˌuː ɹɪsˈɛmbəld sˌʌm ʌv ðoʊz ɪnðə fˈoʊɾəɡɹˌæfz wɜː plˈeɪst ˌʌndɚ sɚvˈeɪləns æt ðə tɹˈeɪd mˈɑːɹt.
+DUMMY1/LJ019-0052.wav|bˈoʊθ sˈɪstəmz kˈeɪm tʊ ˌʌs fɹʌmðə juːnˈaɪɾᵻd stˈeɪts. ðə dˈɪfɹəns wʌz ɹˈiəli mˈoːɹ ɪn dɪɡɹˈiː ðɐn ɪn pɹˈɪnsɪpəl,
+DUMMY1/LJ037-0081.wav|lˈeɪɾɚɹ ɪnðə dˈeɪ ˈiːtʃ wˈʊmən fˈaʊnd ɐn ˈɛmpti ʃˈɛl ɑːnðə ɡɹˈaʊnd nˌɪɹ ðə hˈaʊs. ðiːz tˈuː ʃˈɛlz wɜː dɪlˈɪvɚd tə ðə pəlˈiːs.
+DUMMY1/LJ048-0200.wav|pˈeɪɪŋ pɚtˈɪkjʊlɚɹ ɐtˈɛnʃən tə ðə kɹˈaʊd fɔːɹ ˌɛni ʌnjˈuːʒuːəl æktˈɪvɪɾi.
+DUMMY1/LJ016-0426.wav|kˈʌm ɐlˈɑːŋ, ɡˈæloʊz.
+DUMMY1/LJ008-0182.wav|ɐ tɹəmˈɛndəs kɹˈaʊd ɐsˈɛmbəld wɛn bˈɛlɪŋˌæm wʌz ˈɛksɪkjˌuːɾᵻd ɪn eɪtˈiːn twˈɛlv fɚðə mˈɜːdɚɹ ʌv spˈɛnsɚ pˈɜːsɪvəl, æt ðæt tˈaɪm pɹˈaɪm mˈɪnɪstɚ;
+DUMMY1/LJ043-0107.wav|əpˌɑːn mˈuːvɪŋ tə nˈuː ˈɔːɹliənz ˌɑːn ˈeɪpɹəl twˈɛntifˈoːɹ, naɪntˈiːn sˈɪkstiθɹˈiː,
+DUMMY1/LJ006-0084.wav|ænd sˌoʊ nˈuːmɚɹəs wɜː hɪz ɑːpɚtˈuːnɪɾiz ʌv ʃˈoʊɪŋ fˈeɪvɚɹᵻtˌɪzəm, ðæt ˈɔːl ðə pɹˈɪzənɚz mˈeɪ biː sˈɛd təbi ɪn hɪz pˈaʊɚ.
+DUMMY1/LJ025-0081.wav|hɐz nˈoʊ pˈɜːmənənt daɪdʒˈɛstɪv kˈævɪɾi ɔːɹ mˈaʊθ, bˌʌt tˈeɪks ɪn ɪts fˈuːd ˈɛnɪwˌɛɹ ænd daɪdʒˈɛsts, sˌoʊ tə spˈiːk, ˈɔːl ˌoʊvɚɹ ɪts bˈɑːdi.
+DUMMY1/LJ019-0042.wav|ðiːz wɜːɹ ˈiːðɚ sˈæɾɪsfˌaɪd wɪð ɐ mˈeɪkʃɪft, ænd mˈɑːdɪfˌaɪd ɛɡzˈɪstɪŋ bˈɪldɪŋz, wɪðˌaʊt klˈoʊs ɹɪɡˈɑːɹd tə ðɛɹ sˌuːɾəbˈɪlɪɾi, ɔːɹ fɚɹə lˈɑːŋ tˈaɪm dˈɪd nˈʌθɪŋ æt ˈɔːl.
+DUMMY1/LJ047-0240.wav|ðeɪ ɐɡɹˈiː ðæt hˈɑːsti tˈoʊld ɹɪvˈɪl
+DUMMY1/LJ032-0012.wav|ðə ɹɪsˈɪstəns tʊ ɐɹˈɛst ænd ðɪ ɐtˈɛmptᵻd ʃˈuːɾɪŋ ʌv ɐnˈʌðɚ pəlˈiːs ˈɑːfɪsɚ baɪ ðə mˈæn lˈiː hˈɑːɹvi ˈɑːswəld sˈʌbsɪkwəntli ɐkjˈuːzd ʌv ɐsˈæsᵻnˌeɪɾɪŋ pɹˈɛzɪdənt kˈɛnədi
+DUMMY1/LJ050-0209.wav|ðɪ ɐsˈɪstənt tə ðə dɚɹˈɛktɚɹ ʌvðɪ ˌɛfbˌiːˈaɪ tˈɛstɪfˌaɪd ðˈæt

vits/filelists/ljs_audio_text_val_filelist.txt

@@ -0,0 +1,100 @@
+DUMMY1/LJ022-0023.wav|The overwhelming majority of people in this country know how to sift the wheat from the chaff in what they hear and what they read.
+DUMMY1/LJ043-0030.wav|If somebody did that to me, a lousy trick like that, to take my wife away, and all the furniture, I would be mad as hell, too.
+DUMMY1/LJ005-0201.wav|as is shown by the report of the Commissioners to inquire into the state of the municipal corporations in eighteen thirty-five.
+DUMMY1/LJ001-0110.wav|Even the Caslon type when enlarged shows great shortcomings in this respect:
+DUMMY1/LJ003-0345.wav|All the committee could do in this respect was to throw the responsibility on others.
+DUMMY1/LJ007-0154.wav|These pungent and well-grounded strictures applied with still greater force to the unconvicted prisoner, the man who came to the prison innocent, and still uncontaminated,
+DUMMY1/LJ018-0098.wav|and recognized as one of the frequenters of the bogus law-stationers. His arrest led to that of others.
+DUMMY1/LJ047-0044.wav|Oswald was, however, willing to discuss his contacts with Soviet authorities. He denied having any involvement with Soviet intelligence agencies
+DUMMY1/LJ031-0038.wav|The first physician to see the President at Parkland Hospital was Dr. Charles J. Carrico, a resident in general surgery.
+DUMMY1/LJ048-0194.wav|during the morning of November twenty-two prior to the motorcade.
+DUMMY1/LJ049-0026.wav|On occasion the Secret Service has been permitted to have an agent riding in the passenger compartment with the President.
+DUMMY1/LJ004-0152.wav|although at Mr. Buxton's visit a new jail was in process of erection, the first step towards reform since Howard's visitation in seventeen seventy-four.
+DUMMY1/LJ008-0278.wav|or theirs might be one of many, and it might be considered necessary to "make an example."
+DUMMY1/LJ043-0002.wav|The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy. Chapter seven. Lee Harvey Oswald:
+DUMMY1/LJ009-0114.wav|Mr. Wakefield winds up his graphic but somewhat sensational account by describing another religious service, which may appropriately be inserted here.
+DUMMY1/LJ028-0506.wav|A modern artist would have difficulty in doing such accurate work.
+DUMMY1/LJ050-0168.wav|with the particular purposes of the agency involved. The Commission recognizes that this is a controversial area
+DUMMY1/LJ039-0223.wav|Oswald's Marine training in marksmanship, his other rifle experience and his established familiarity with this particular weapon
+DUMMY1/LJ029-0032.wav|According to O'Donnell, quote, we had a motorcade wherever we went, end quote.
+DUMMY1/LJ031-0070.wav|Dr. Clark, who most closely observed the head wound,
+DUMMY1/LJ034-0198.wav|Euins, who was on the southwest corner of Elm and Houston Streets testified that he could not describe the man he saw in the window.
+DUMMY1/LJ026-0068.wav|Energy enters the plant, to a small extent,
+DUMMY1/LJ039-0075.wav|once you know that you must put the crosshairs on the target and that is all that is necessary.
+DUMMY1/LJ004-0096.wav|the fatal consequences whereof might be prevented if the justices of the peace were duly authorized
+DUMMY1/LJ005-0014.wav|Speaking on a debate on prison matters, he declared that
+DUMMY1/LJ012-0161.wav|he was reported to have fallen away to a shadow.
+DUMMY1/LJ018-0239.wav|His disappearance gave color and substance to evil reports already in circulation that the will and conveyance above referred to
+DUMMY1/LJ019-0257.wav|Here the tread-wheel was in use, there cellular cranks, or hard-labor machines.
+DUMMY1/LJ028-0008.wav|you tap gently with your heel upon the shoulder of the dromedary to urge her on.
+DUMMY1/LJ024-0083.wav|This plan of mine is no attack on the Court;
+DUMMY1/LJ042-0129.wav|No night clubs or bowling alleys, no places of recreation except the trade union dances. I have had enough.
+DUMMY1/LJ036-0103.wav|The police asked him whether he could pick out his passenger from the lineup.
+DUMMY1/LJ046-0058.wav|During his Presidency, Franklin D. Roosevelt made almost four hundred journeys and traveled more than three hundred fifty thousand miles.
+DUMMY1/LJ014-0076.wav|He was seen afterwards smoking and talking with his hosts in their back parlor, and never seen again alive.
+DUMMY1/LJ002-0043.wav|long narrow rooms -- one thirty-six feet, six twenty-three feet, and the eighth eighteen,
+DUMMY1/LJ009-0076.wav|We come to the sermon.
+DUMMY1/LJ017-0131.wav|even when the high sheriff had told him there was no possibility of a reprieve, and within a few hours of execution.
+DUMMY1/LJ046-0184.wav|but there is a system for the immediate notification of the Secret Service by the confining institution when a subject is released or escapes.
+DUMMY1/LJ014-0263.wav|When other pleasures palled he took a theatre, and posed as a munificent patron of the dramatic art.
+DUMMY1/LJ042-0096.wav|(old exchange rate) in addition to his factory salary of approximately equal amount
+DUMMY1/LJ049-0050.wav|Hill had both feet on the car and was climbing aboard to assist President and Mrs. Kennedy.
+DUMMY1/LJ019-0186.wav|seeing that since the establishment of the Central Criminal Court, Newgate received prisoners for trial from several counties,
+DUMMY1/LJ028-0307.wav|then let twenty days pass, and at the end of that time station near the Chaldasan gates a body of four thousand.
+DUMMY1/LJ012-0235.wav|While they were in a state of insensibility the murder was committed.
+DUMMY1/LJ034-0053.wav|reached the same conclusion as Latona that the prints found on the cartons were those of Lee Harvey Oswald.
+DUMMY1/LJ014-0030.wav|These were damnatory facts which well supported the prosecution.
+DUMMY1/LJ015-0203.wav|but were the precautions too minute, the vigilance too close to be eluded or overcome?
+DUMMY1/LJ028-0093.wav|but his scribe wrote it in the manner customary for the scribes of those days to write of their royal masters.
+DUMMY1/LJ002-0018.wav|The inadequacy of the jail was noticed and reported upon again and again by the grand juries of the city of London,
+DUMMY1/LJ028-0275.wav|At last, in the twentieth month,
+DUMMY1/LJ012-0042.wav|which he kept concealed in a hiding-place with a trap-door just under his bed.
+DUMMY1/LJ011-0096.wav|He married a lady also belonging to the Society of Friends, who brought him a large fortune, which, and his own money, he put into a city firm,
+DUMMY1/LJ036-0077.wav|Roger D. Craig, a deputy sheriff of Dallas County,
+DUMMY1/LJ016-0318.wav|Other officials, great lawyers, governors of prisons, and chaplains supported this view.
+DUMMY1/LJ013-0164.wav|who came from his room ready dressed, a suspicious circumstance, as he was always late in the morning.
+DUMMY1/LJ027-0141.wav|is closely reproduced in the life-history of existing deer. Or, in other words,
+DUMMY1/LJ028-0335.wav|accordingly they committed to him the command of their whole army, and put the keys of their city into his hands.
+DUMMY1/LJ031-0202.wav|Mrs. Kennedy chose the hospital in Bethesda for the autopsy because the President had served in the Navy.
+DUMMY1/LJ021-0145.wav|From those willing to join in establishing this hoped-for period of peace,
+DUMMY1/LJ016-0288.wav|"Müller, Müller, He's the man," till a diversion was created by the appearance of the gallows, which was received with continuous yells.
+DUMMY1/LJ028-0081.wav|Years later, when the archaeologists could readily distinguish the false from the true,
+DUMMY1/LJ018-0081.wav|his defense being that he had intended to commit suicide, but that, on the appearance of this officer who had wronged him,
+DUMMY1/LJ021-0066.wav|together with a great increase in the payrolls, there has come a substantial rise in the total of industrial profits
+DUMMY1/LJ009-0238.wav|After this the sheriffs sent for another rope, but the spectators interfered, and the man was carried back to jail.
+DUMMY1/LJ005-0079.wav|and improve the morals of the prisoners, and shall insure the proper measure of punishment to convicted offenders.
+DUMMY1/LJ035-0019.wav|drove to the northwest corner of Elm and Houston, and parked approximately ten feet from the traffic signal.
+DUMMY1/LJ036-0174.wav|This is the approximate time he entered the roominghouse, according to Earlene Roberts, the housekeeper there.
+DUMMY1/LJ046-0146.wav|The criteria in effect prior to November twenty-two, nineteen sixty-three, for determining whether to accept material for the PRS general files
+DUMMY1/LJ017-0044.wav|and the deepest anxiety was felt that the crime, if crime there had been, should be brought home to its perpetrator.
+DUMMY1/LJ017-0070.wav|but his sporting operations did not prosper, and he became a needy man, always driven to desperate straits for cash.
+DUMMY1/LJ014-0020.wav|He was soon afterwards arrested on suspicion, and a search of his lodgings brought to light several garments saturated with blood;
+DUMMY1/LJ016-0020.wav|He never reached the cistern, but fell back into the yard, injuring his legs severely.
+DUMMY1/LJ045-0230.wav|when he was finally apprehended in the Texas Theatre. Although it is not fully corroborated by others who were present,
+DUMMY1/LJ035-0129.wav|and she must have run down the stairs ahead of Oswald and would probably have seen or heard him.
+DUMMY1/LJ008-0307.wav|afterwards express a wish to murder the Recorder for having kept them so long in suspense.
+DUMMY1/LJ008-0294.wav|nearly indefinitely deferred.
+DUMMY1/LJ047-0148.wav|On October twenty-five,
+DUMMY1/LJ008-0111.wav|They entered a "stone cold room," and were presently joined by the prisoner.
+DUMMY1/LJ034-0042.wav|that he could only testify with certainty that the print was less than three days old.
+DUMMY1/LJ037-0234.wav|Mrs. Mary Brock, the wife of a mechanic who worked at the station, was there at the time and she saw a white male,
+DUMMY1/LJ040-0002.wav|Chapter seven. Lee Harvey Oswald: Background and Possible Motives, Part one.
+DUMMY1/LJ045-0140.wav|The arguments he used to justify his use of the alias suggest that Oswald may have come to think that the whole world was becoming involved
+DUMMY1/LJ012-0035.wav|the number and names on watches, were carefully removed or obliterated after the goods passed out of his hands.
+DUMMY1/LJ012-0250.wav|On the seventh July, eighteen thirty-seven,
+DUMMY1/LJ016-0179.wav|contracted with sheriffs and conveners to work by the job.
+DUMMY1/LJ016-0138.wav|at a distance from the prison.
+DUMMY1/LJ027-0052.wav|These principles of homology are essential to a correct interpretation of the facts of morphology.
+DUMMY1/LJ031-0134.wav|On one occasion Mrs. Johnson, accompanied by two Secret Service agents, left the room to see Mrs. Kennedy and Mrs. Connally.
+DUMMY1/LJ019-0273.wav|which Sir Joshua Jebb told the committee he considered the proper elements of penal discipline.
+DUMMY1/LJ014-0110.wav|At the first the boxes were impounded, opened, and found to contain many of O'Connor's effects.
+DUMMY1/LJ034-0160.wav|on Brennan's subsequent certain identification of Lee Harvey Oswald as the man he saw fire the rifle.
+DUMMY1/LJ038-0199.wav|eleven. If I am alive and taken prisoner,
+DUMMY1/LJ014-0010.wav|yet he could not overcome the strange fascination it had for him, and remained by the side of the corpse till the stretcher came.
+DUMMY1/LJ033-0047.wav|I noticed when I went out that the light was on, end quote,
+DUMMY1/LJ040-0027.wav|He was never satisfied with anything.
+DUMMY1/LJ048-0228.wav|and others who were present say that no agent was inebriated or acted improperly.
+DUMMY1/LJ003-0111.wav|He was in consequence put out of the protection of their internal law, end quote. Their code was a subject of some curiosity.
+DUMMY1/LJ008-0258.wav|Let me retrace my steps, and speak more in detail of the treatment of the condemned in those bloodthirsty and brutally indifferent days,
+DUMMY1/LJ029-0022.wav|The original plan called for the President to spend only one day in the State, making whirlwind visits to Dallas, Fort Worth, San Antonio, and Houston.
+DUMMY1/LJ004-0045.wav|Mr. Sturges Bourne, Sir James Mackintosh, Sir James Scarlett, and William Wilberforce.

vits/filelists/ljs_audio_text_val_filelist.txt.cleaned

@@ -0,0 +1,100 @@
+DUMMY1/LJ022-0023.wav|ðɪ ˌoʊvɚwˈɛlmɪŋ mədʒˈɔːɹɪɾi ʌv pˈiːpəl ɪn ðɪs kˈʌntɹi nˈoʊ hˌaʊ tə sˈɪft ðə wˈiːt fɹʌmðə tʃˈæf ɪn wˌʌt ðeɪ hˈɪɹ ænd wˌʌt ðeɪ ɹˈiːd.
+DUMMY1/LJ043-0030.wav|ɪf sˈʌmbɑːdi dˈɪd ðˈæt tə mˌiː, ɐ lˈaʊsi tɹˈɪk lˈaɪk ðˈæt, tə tˈeɪk maɪ wˈaɪf ɐwˈeɪ, ænd ˈɔːl ðə fˈɜːnɪtʃɚ, ˈaɪ wʊd biː mˈæd æz hˈɛl, tˈuː.
+DUMMY1/LJ005-0201.wav|ˌæzˌɪz ʃˈoʊn baɪ ðə ɹɪpˈoːɹt ʌvðə kəmˈɪʃənɚz tʊ ɪnkwˈaɪɚɹ ˌɪntʊ ðə stˈeɪt ʌvðə mjuːnˈɪsɪpəl kˌɔːɹpɚɹˈeɪʃənz ɪn eɪtˈiːn θˈɜːɾifˈaɪv.
+DUMMY1/LJ001-0110.wav|ˈiːvən ðə kˈæslɑːn tˈaɪp wɛn ɛnlˈɑːɹdʒd ʃˈoʊz ɡɹˈeɪt ʃˈɔːɹtkʌmɪŋz ɪn ðɪs ɹɪspˈɛkt:
+DUMMY1/LJ003-0345.wav|ˈɔːl ðə kəmˈɪɾi kʊd dˈuː ɪn ðɪs ɹɪspˈɛkt wʌz tə θɹˈoʊ ðə ɹɪspˌɑːnsəbˈɪlɪɾi ˌɑːn ˈʌðɚz.
+DUMMY1/LJ007-0154.wav|ðiːz pˈʌndʒənt ænd wˈɛlɡɹˈaʊndᵻd stɹˈɪktʃɚz ɐplˈaɪd wɪð stˈɪl ɡɹˈeɪɾɚ fˈoːɹs tə ðɪ ʌnkənvˈɪktᵻd pɹˈɪzənɚ, ðə mˈæn hˌuː kˈeɪm tə ðə pɹˈɪzən ˈɪnəsənt, ænd stˈɪl ʌnkəntˈæmᵻnˌeɪɾᵻd,
+DUMMY1/LJ018-0098.wav|ænd ɹˈɛkəɡnˌaɪzd æz wˈʌn ʌvðə fɹˈiːkwɛntɚz ʌvðə bˈoʊɡəs lˈɔːstˈeɪʃənɚz. hɪz ɐɹˈɛst lˈɛd tə ðæt ʌv ˈʌðɚz.
+DUMMY1/LJ047-0044.wav|ˈɑːswəld wʌz, haʊˈɛvɚ, wˈɪlɪŋ tə dɪskˈʌs hɪz kˈɑːntækts wɪð sˈoʊviət ɐθˈɔːɹɪɾiz. hiː dɪnˈaɪd hˌævɪŋ ˌɛni ɪnvˈɑːlvmənt wɪð sˈoʊviət ɪntˈɛlɪdʒəns ˈeɪdʒənsiz
+DUMMY1/LJ031-0038.wav|ðə fˈɜːst fɪzˈɪʃən tə sˈiː ðə pɹˈɛzɪdənt æt pˈɑːɹklənd hˈɑːspɪɾəl wʌz dˈɑːktɚ tʃˈɑːɹlz dʒˈeɪ. kˈæɹɪkˌoʊ, ɐ ɹˈɛzɪdənt ɪn dʒˈɛnɚɹəl sˈɜːdʒɚɹi.
+DUMMY1/LJ048-0194.wav|dˈʊɹɪŋ ðə mˈɔːɹnɪŋ ʌv noʊvˈɛmbɚ twˈɛntitˈuː pɹˈaɪɚ tə ðə mˈoʊɾɚkˌeɪd.
+DUMMY1/LJ049-0026.wav|ˌɑːn əkˈeɪʒən ðə sˈiːkɹət sˈɜːvɪs hɐzbɪn pɚmˈɪɾᵻd tə hæv ɐn ˈeɪdʒənt ɹˈaɪdɪŋ ɪnðə pˈæsɪndʒɚ kəmpˈɑːɹtmənt wɪððə pɹˈɛzɪdənt.
+DUMMY1/LJ004-0152.wav|ɑːlðˈoʊ æt mˈɪstɚ bˈʌkstənz vˈɪzɪt ɐ nˈuː dʒˈeɪl wʌz ɪn pɹˈɑːsɛs ʌv ɪɹˈɛkʃən, ðə fˈɜːst stˈɛp tʊwˈɔːɹdz ɹɪfˈɔːɹm sˈɪns hˈaʊɚdz vˌɪzɪtˈeɪʃən ɪn sˌɛvəntˈiːn sˈɛvəntifˈoːɹ.
+DUMMY1/LJ008-0278.wav|ɔːɹ ðˈɛɹz mˌaɪt biː wˈʌn ʌv mˈɛni, ænd ɪt mˌaɪt biː kənsˈɪdɚd nˈɛsəsɚɹi tuː "mˌeɪk ɐn ɛɡzˈæmpəl."
+DUMMY1/LJ043-0002.wav|ðə wˈɔːɹən kəmˈɪʃən ɹɪpˈoːɹt. baɪ ðə pɹˈɛzɪdənts kəmˈɪʃən ɑːnðɪ ɐsˌæsᵻnˈeɪʃən ʌv pɹˈɛzɪdənt kˈɛnədi. tʃˈæptɚ sˈɛvən. lˈiː hˈɑːɹvi ˈɑːswəld:
+DUMMY1/LJ009-0114.wav|mˈɪstɚ wˈeɪkfiːld wˈaɪndz ˈʌp hɪz ɡɹˈæfɪk bˌʌt sˈʌmwʌt sɛnsˈeɪʃənəl ɐkˈaʊnt baɪ dɪskɹˈaɪbɪŋ ɐnˈʌðɚ ɹɪlˈɪdʒəs sˈɜːvɪs, wˌɪtʃ mˈeɪ ɐpɹˈoʊpɹɪətli biː ɪnsˈɜːɾᵻd hˈɪɹ.
+DUMMY1/LJ028-0506.wav|ɐ mˈɑːdɚn ˈɑːɹɾɪst wʊdhɐv dˈɪfɪkˌʌlti ɪn dˌuːɪŋ sˈʌtʃ ˈækjʊɹət wˈɜːk.
+DUMMY1/LJ050-0168.wav|wɪððə pɚtˈɪkjʊlɚ pˈɜːpəsᵻz ʌvðɪ ˈeɪdʒənsi ɪnvˈɑːlvd. ðə kəmˈɪʃən ɹˈɛkəɡnˌaɪzɪz ðæt ðɪs ɪz ɐ kˌɑːntɹəvˈɜːʃəl ˈɛɹiə
+DUMMY1/LJ039-0223.wav|ˈɑːswəldz mɚɹˈiːn tɹˈeɪnɪŋ ɪn mˈɑːɹksmənʃˌɪp, hɪz ˈʌðɚ ɹˈaɪfəl ɛkspˈiəɹɪəns ænd hɪz ɪstˈæblɪʃt fəmˌɪlɪˈæɹɪɾi wɪð ðɪs pɚtˈɪkjʊlɚ wˈɛpən
+DUMMY1/LJ029-0032.wav|ɐkˈoːɹdɪŋ tʊ oʊdˈɑːnəl, kwˈoʊt, wiː hɐd ɐ mˈoʊɾɚkˌeɪd wɛɹɹˈɛvɚ wiː wˈɛnt, ˈɛnd kwˈoʊt.
+DUMMY1/LJ031-0070.wav|dˈɑːktɚ klˈɑːɹk, hˌuː mˈoʊst klˈoʊsli ɑːbzˈɜːvd ðə hˈɛd wˈuːnd,
+DUMMY1/LJ034-0198.wav|jˈuːɪnz, hˌuː wʌz ɑːnðə saʊθwˈɛst kˈɔːɹnɚɹ ʌv ˈɛlm ænd hjˈuːstən stɹˈiːts tˈɛstɪfˌaɪd ðæt hiː kʊd nˌɑːt dɪskɹˈaɪb ðə mˈæn hiː sˈɔː ɪnðə wˈɪndoʊ.
+DUMMY1/LJ026-0068.wav|ˈɛnɚdʒi ˈɛntɚz ðə plˈænt, tʊ ɐ smˈɔːl ɛkstˈɛnt,
+DUMMY1/LJ039-0075.wav|wˈʌns juː nˈoʊ ðæt juː mˈʌst pˌʊt ðə kɹˈɔshɛɹz ɑːnðə tˈɑːɹɡɪt ænd ðæt ɪz ˈɔːl ðæt ɪz nˈɛsəsɚɹi.
+DUMMY1/LJ004-0096.wav|ðə fˈeɪɾəl kˈɑːnsɪkwənsᵻz wˈɛɹɑːf mˌaɪt biː pɹɪvˈɛntᵻd ɪf ðə dʒˈʌstɪsᵻz ʌvðə pˈiːs wɜː djˈuːli ˈɔːθɚɹˌaɪzd
+DUMMY1/LJ005-0014.wav|spˈiːkɪŋ ˌɑːn ɐ dɪbˈeɪt ˌɑːn pɹˈɪzən mˈæɾɚz, hiː dᵻklˈɛɹd ðˈæt
+DUMMY1/LJ012-0161.wav|hiː wʌz ɹɪpˈoːɹɾᵻd tə hæv fˈɔːlən ɐwˈeɪ tʊ ɐ ʃˈædoʊ.
+DUMMY1/LJ018-0239.wav|hɪz dˌɪsɐpˈɪɹəns ɡˈeɪv kˈʌlɚ ænd sˈʌbstəns tʊ ˈiːvəl ɹɪpˈoːɹts ɔːlɹˌɛdi ɪn sˌɜːkjʊlˈeɪʃən ðætðə wɪl ænd kənvˈeɪəns əbˌʌv ɹɪfˈɜːd tuː
+DUMMY1/LJ019-0257.wav|hˈɪɹ ðə tɹˈɛdw��iːl wʌz ɪn jˈuːs, ðɛɹ sˈɛljʊlɚ kɹˈæŋks, ɔːɹ hˈɑːɹdlˈeɪbɚ məʃˈiːnz.
+DUMMY1/LJ028-0008.wav|juː tˈæp dʒˈɛntli wɪð jʊɹ hˈiːl əpˌɑːn ðə ʃˈoʊldɚɹ ʌvðə dɹˈoʊmdɚɹi tʊ ˈɜːdʒ hɜːɹ ˈɑːn.
+DUMMY1/LJ024-0083.wav|ðɪs plˈæn ʌv mˈaɪn ɪz nˈoʊ ɐtˈæk ɑːnðə kˈoːɹt;
+DUMMY1/LJ042-0129.wav|nˈoʊ nˈaɪt klˈʌbz ɔːɹ bˈoʊlɪŋ ˈælɪz, nˈoʊ plˈeɪsᵻz ʌv ɹˌɛkɹiːˈeɪʃən ɛksˈɛpt ðə tɹˈeɪd jˈuːniən dˈænsᵻz. ˈaɪ hæv hɐd ɪnˈʌf.
+DUMMY1/LJ036-0103.wav|ðə pəlˈiːs ˈæskt hˌɪm wˈɛðɚ hiː kʊd pˈɪk ˈaʊt hɪz pˈæsɪndʒɚ fɹʌmðə lˈaɪnʌp.
+DUMMY1/LJ046-0058.wav|dˈʊɹɪŋ hɪz pɹˈɛzɪdənsi, fɹˈæŋklɪn dˈiː. ɹˈoʊzəvˌɛlt mˌeɪd ˈɔːlmoʊst fˈoːɹ hˈʌndɹəd dʒˈɜːnɪz ænd tɹˈævəld mˈoːɹ ðɐn θɹˈiː hˈʌndɹəd fˈɪfti θˈaʊzənd mˈaɪlz.
+DUMMY1/LJ014-0076.wav|hiː wʌz sˈiːn ˈæftɚwɚdz smˈoʊkɪŋ ænd tˈɔːkɪŋ wɪð hɪz hˈoʊsts ɪn ðɛɹ bˈæk pˈɑːɹlɚ, ænd nˈɛvɚ sˈiːn ɐɡˈɛn ɐlˈaɪv.
+DUMMY1/LJ002-0043.wav|lˈɑːŋ nˈæɹoʊ ɹˈuːmz wˈʌn θˈɜːɾisˈɪks fˈiːt, sˈɪks twˈɛntiθɹˈiː fˈiːt, ænd ðɪ ˈeɪtθ eɪtˈiːn,
+DUMMY1/LJ009-0076.wav|wiː kˈʌm tə ðə sˈɜːmən.
+DUMMY1/LJ017-0131.wav|ˈiːvən wɛn ðə hˈaɪ ʃˈɛɹɪf hɐd tˈoʊld hˌɪm ðɛɹwˌʌz nˈoʊ pˌɑːsəbˈɪlɪɾi əvɚ ɹɪpɹˈiːv, ænd wɪðˌɪn ɐ fjˈuː ˈaɪʊɹz ʌv ˌɛksɪkjˈuːʃən.
+DUMMY1/LJ046-0184.wav|bˌʌt ðɛɹ ɪz ɐ sˈɪstəm fɚðɪ ɪmˈiːdɪət nˌoʊɾɪfɪkˈeɪʃən ʌvðə sˈiːkɹət sˈɜːvɪs baɪ ðə kənfˈaɪnɪŋ ˌɪnstɪtˈuːʃən wɛn ɐ sˈʌbdʒɛkt ɪz ɹɪlˈiːsd ɔːɹ ɛskˈeɪps.
+DUMMY1/LJ014-0263.wav|wˌɛn ˈʌðɚ plˈɛʒɚz pˈɔːld hiː tˈʊk ɐ θˈiəɾɚ, ænd pˈoʊzd æz ɐ mjuːnˈɪfɪsənt pˈeɪtɹən ʌvðə dɹəmˈæɾɪk ˈɑːɹt.
+DUMMY1/LJ042-0096.wav| ˈoʊld ɛkstʃˈeɪndʒ ɹˈeɪt ɪn ɐdˈɪʃən tə hɪz fˈæktɚɹi sˈælɚɹi ʌv ɐpɹˈɑːksɪmətli ˈiːkwəl ɐmˈaʊnt
+DUMMY1/LJ049-0050.wav|hˈɪl hɐd bˈoʊθ fˈiːt ɑːnðə kˈɑːɹ ænd wʌz klˈaɪmɪŋ ɐbˈoːɹd tʊ ɐsˈɪst pɹˈɛzɪdənt ænd mɪsˈɛs kˈɛnədi.
+DUMMY1/LJ019-0186.wav|sˈiːɪŋ ðæt sˈɪns ðɪ ɪstˈæblɪʃmənt ʌvðə sˈɛntɹəl kɹˈɪmɪnəl kˈoːɹt, nˈuːɡeɪt ɹɪsˈiːvd pɹˈɪzənɚz fɔːɹ tɹˈaɪəl fɹʌm sˈɛvɹəl kˈaʊntɪz,
+DUMMY1/LJ028-0307.wav|ðˈɛn lˈɛt twˈɛnti dˈeɪz pˈæs, ænd æt ðɪ ˈɛnd ʌv ðæt tˈaɪm stˈeɪʃən nˌɪɹ ðə tʃˈældæsən ɡˈeɪts ɐ bˈɑːdi ʌv fˈoːɹ θˈaʊzənd.
+DUMMY1/LJ012-0235.wav|wˌaɪl ðeɪ wɜːɹ ɪn ɐ stˈeɪt ʌv ɪnsˌɛnsəbˈɪlɪɾi ðə mˈɜːdɚ wʌz kəmˈɪɾᵻd.
+DUMMY1/LJ034-0053.wav|ɹˈiːtʃt ðə sˈeɪm kənklˈuːʒən æz lætˈoʊnə ðætðə pɹˈɪnts fˈaʊnd ɑːnðə kˈɑːɹtənz wɜː ðoʊz ʌv lˈiː hˈɑːɹvi ˈɑːswəld.
+DUMMY1/LJ014-0030.wav|ðiːz wɜː dˈæmnətˌoːɹi fˈækts wˌɪtʃ wˈɛl səpˈoːɹɾᵻd ðə pɹˌɑːsɪkjˈuːʃən.
+DUMMY1/LJ015-0203.wav|bˌʌt wɜː ðə pɹɪkˈɔːʃənz tˈuː mˈɪnɪt, ðə vˈɪdʒɪləns tˈuː klˈoʊs təbi ɪlˈuːdᵻd ɔːɹ ˌoʊvɚkˈʌm?
+DUMMY1/LJ028-0093.wav|bˌʌt hɪz skɹˈaɪb ɹˈoʊt ɪt ɪnðə mˈænɚ kˈʌstəmˌɛɹi fɚðə skɹˈaɪbz ʌv ðoʊz dˈeɪz tə ɹˈaɪt ʌv ðɛɹ ɹˈɔɪəl mˈæstɚz.
+DUMMY1/LJ002-0018.wav|ðɪ ɪnˈædɪkwəsi ʌvðə dʒˈeɪl wʌz nˈoʊɾɪsd ænd ɹɪpˈoːɹɾᵻd əpˌɑːn ɐɡˈɛn ænd ɐɡˈɛn baɪ ðə ɡɹˈænd dʒˈʊɹɪz ʌvðə sˈɪɾi ʌv lˈʌndən,
+DUMMY1/LJ028-0275.wav|æt lˈæst, ɪnðə twˈɛntiəθ mˈʌnθ,
+DUMMY1/LJ012-0042.wav|wˌɪtʃ hiː kˈɛpt kənsˈiːld ɪn ɐ hˈaɪdɪŋplˈeɪs wɪð ɐ tɹˈæpdˈoːɹ dʒˈʌst ˌʌndɚ hɪz bˈɛd.
+DUMMY1/LJ011-0096.wav|hiː mˈæɹɪd ɐ lˈeɪdi ˈɑːlsoʊ bɪlˈɑːŋɪŋ tə ðə səsˈaɪəɾi ʌv fɹˈɛndz, hˌuː bɹˈɔːt hˌɪm ɐ lˈɑːɹdʒ fˈɔːɹtʃən, wˈɪtʃ, ænd hɪz ˈoʊn mˈʌni, hiː pˌʊt ˌɪntʊ ɐ sˈɪɾi fˈɜːm,
+DUMMY1/LJ036-0077.wav|ɹˈɑːdʒɚ dˈiː. kɹˈeɪɡ, ɐ dˈɛpjuːɾi ʃˈɛɹɪf ʌv dˈæləs kˈaʊnti,
+DUMMY1/LJ016-0318.wav|ˈʌðɚɹ əfˈɪʃəlz, ɡɹˈeɪt lˈɔɪɚz, ɡˈʌvɚnɚz ʌv pɹˈɪzənz, ænd tʃˈæplɪnz səpˈoːɹɾᵻd ðɪs vjˈuː.
+DUMMY1/LJ013-0164.wav|hˌuː kˈeɪm fɹʌm hɪz ɹˈuːm ɹˈɛdi dɹˈɛst, ɐ səspˈɪʃəs sˈɜːkəmstˌæns, æz hiː wʌz ˈɔːlweɪz lˈeɪt ɪnðə mˈɔːɹnɪŋ.
+DUMMY1/LJ027-0141.wav|ɪz klˈoʊsli ɹɪpɹədˈuːst ɪnðə lˈaɪfhˈɪstɚɹi ʌv ɛɡzˈɪstɪŋ dˈɪɹ. ˈɔːɹ, ɪn ˈʌðɚ wˈɜːdz,
+DUMMY1/LJ028-0335.wav|ɐkˈoːɹdɪŋli ðeɪ kəmˈɪɾᵻd tə hˌɪm ðə kəmˈænd ʌv ðɛɹ hˈoʊl ˈɑːɹmi, ænd pˌʊt ðə kˈiːz ʌv ðɛɹ sˈɪɾi ˌɪntʊ hɪz hˈændz.
+DUMMY1/LJ031-0202.wav|mɪsˈɛs kˈɛnədi tʃˈoʊz ðə hˈɑːspɪɾəl ɪn bəθˈɛzdə fɚðɪ ˈɔːtɑːpsi bɪkˈʌz ðə pɹˈɛzɪdənt hɐd sˈɜːvd ɪnðə nˈeɪvi.
+DUMMY1/LJ021-0145.wav|fɹʌm ðoʊz wˈɪlɪŋ tə dʒˈɔɪn ɪn ɪstˈæblɪʃɪŋ ðɪs hˈoʊptfɔːɹ pˈiəɹɪəd ʌv pˈiːs,
+DUMMY1/LJ016-0288.wav|"mˈʌlɚ, mˈʌlɚ, hiːz ðə mˈæn," tˈɪl ɐ daɪvˈɜːʒən wʌz kɹiːˈeɪɾᵻd baɪ ðɪ ɐpˈɪɹəns ʌvðə ɡˈæloʊz, wˌɪtʃ wʌz ɹɪsˈiːvd wɪð kəntˈɪnjuːəs jˈɛlz.
+DUMMY1/LJ028-0081.wav|jˈɪɹz lˈeɪɾɚ, wˌɛn ðɪ ˌɑːɹkiːˈɑːlədʒˌɪsts kʊd ɹˈɛdɪli dɪstˈɪŋɡwɪʃ ðə fˈɑːls fɹʌmðə tɹˈuː,
+DUMMY1/LJ018-0081.wav|hɪz dɪfˈɛns bˌiːɪŋ ðæt hiː hɐd ɪntˈɛndᵻd tə kəmˈɪt sˈuːɪsˌaɪd, bˌʌt ðˈæt, ɑːnðɪ ɐpˈɪɹəns ʌv ðɪs ˈɑːfɪsɚ hˌuː hɐd ɹˈɔŋd hˌɪm,
+DUMMY1/LJ021-0066.wav|təɡˌɛðɚ wɪð ɐ ɡɹˈeɪt ˈɪnkɹiːs ɪnðə pˈeɪɹoʊlz, ðɛɹ hɐz kˈʌm ɐ səbstˈænʃəl ɹˈaɪz ɪnðə tˈoʊɾəl ʌv ɪndˈʌstɹɪəl pɹˈɑːfɪts
+DUMMY1/LJ009-0238.wav|ˈæftɚ ðɪs ðə ʃˈɛɹɪfs sˈɛnt fɔːɹ ɐnˈʌðɚ ɹˈoʊp, bˌʌt ðə spɛktˈeɪɾɚz ˌɪntəfˈɪɹd, ænd ðə mˈæn wʌz kˈæɹɪd bˈæk tə dʒˈeɪl.
+DUMMY1/LJ005-0079.wav|ænd ɪmpɹˈuːv ðə mˈɔːɹəlz ʌvðə pɹˈɪzənɚz, ænd ʃˌæl ɪnʃˈʊɹ ðə pɹˈɑːpɚ mˈɛʒɚɹ ʌv pˈʌnɪʃmənt tə kənvˈɪktᵻd əfˈɛndɚz.
+DUMMY1/LJ035-0019.wav|dɹˈoʊv tə ðə nɔːɹθwˈɛst kˈɔːɹnɚɹ ʌv ˈɛlm ænd hjˈuːstən, ænd pˈɑːɹkt ɐpɹˈɑːksɪmətli tˈɛn fˈiːt fɹʌmðə tɹˈæfɪk sˈɪɡnəl.
+DUMMY1/LJ036-0174.wav|ðɪs ɪz ðɪ ɐpɹˈɑːksɪmət tˈaɪm hiː ˈɛntɚd ðə ɹˈuːmɪŋhˌaʊs, ɐkˈoːɹdɪŋ tʊ ˈɜːliːn ɹˈɑːbɚts, ðə hˈaʊskiːpɚ ðˈɛɹ.
+DUMMY1/LJ046-0146.wav|ðə kɹaɪtˈiəɹɪə ɪn ɪfˈɛkt pɹˈaɪɚ tə noʊvˈɛmbɚ twˈɛntitˈuː, naɪntˈiːn sˈɪkstiθɹˈiː, fɔːɹ dɪtˈɜːmɪnɪŋ wˈɛðɚ tʊ ɐksˈɛpt mətˈiəɹɪəl fɚðə pˌiːˌɑːɹˈɛs dʒˈɛnɚɹəl fˈaɪlz
+DUMMY1/LJ017-0044.wav|ænd ðə dˈiːpəst æŋzˈaɪəɾi wʌz fˈɛlt ðætðə kɹˈaɪm, ɪf kɹˈaɪm ðˈɛɹ hɐdbɪn, ʃˌʊd biː bɹˈɔːt hˈoʊm tʊ ɪts pˈɜːpɪtɹˌeɪɾɚ.
+DUMMY1/LJ017-0070.wav|bˌʌt hɪz spˈoːɹɾɪŋ ˌɑːpɚɹˈeɪʃənz dɪdnˌɑːt pɹˈɑːspɚ, ænd hiː bɪkˌeɪm ɐ nˈiːdi mˈæn, ˈɔːlweɪz dɹˈɪvən tə dˈɛspɚɹət stɹˈeɪts fɔːɹ kˈæʃ.
+DUMMY1/LJ014-0020.wav|hiː wʌz sˈuːn ˈæftɚwɚdz ɐɹˈɛstᵻd ˌɑːn səspˈɪʃən, ænd ɐ sˈɜːtʃ ʌv hɪz lˈɑːdʒɪŋz bɹˈɔːt tə lˈaɪt sˈɛvɹəl ɡˈɑːɹmənts sˈætʃɚɹˌeɪɾᵻd wɪð blˈʌd;
+DUMMY1/LJ016-0020.wav|hiː nˈɛvɚ ɹˈiːtʃt ðə sˈɪstɚn, bˌʌt fˈɛl bˈæk ˌɪntʊ ðə jˈɑːɹd, ˈɪndʒɚɹɪŋ hɪz lˈɛɡz sɪvˈɪɹli.
+DUMMY1/LJ045-0230.wav|wˌɛn hiː wʌz fˈaɪnəli ˌæpɹɪhˈɛndᵻd ɪnðə tˈɛksəs θˈiəɾɚ. ɑːlðˈoʊ ɪt ɪz nˌɑːt fˈʊli kɚɹˈɑːbɚɹˌeɪɾᵻd baɪ ˈʌðɚz hˌuː wɜː pɹˈɛzənt,
+DUMMY1/LJ035-0129.wav|ænd ʃiː mˈʌstɐv ɹˈʌn dˌaʊn ðə stˈɛɹz ɐhˈɛd ʌv ˈɑːswəld ænd wʊd pɹˈɑːbəbli hæv sˈiːn ɔːɹ hˈɜːd hˌɪm.
+DUMMY1/LJ008-0307.wav|ˈæftɚwɚdz ɛkspɹˈɛs ɐ wˈɪʃ tə mˈɜːdɚ ðə ɹɪkˈoːɹdɚ fɔːɹ hˌævɪŋ kˈɛpt ðˌɛm sˌoʊ lˈɑːŋ ɪn səspˈɛns.
+DUMMY1/LJ008-0294.wav|nˌɪɹli ɪndˈɛfɪnətli dɪfˈɜːd.
+DUMMY1/LJ047-0148.wav|ˌɑːn ɑːktˈoʊbɚ twˈɛntifˈaɪv,
+DUMMY1/LJ008-0111.wav|ðeɪ ˈɛntɚd ˈeɪ "stˈoʊn kˈoʊld ɹˈuːm," ænd wɜː pɹˈɛzəntli dʒˈɔɪnd baɪ ðə pɹˈɪzənɚ.
+DUMMY1/LJ034-0042.wav|ðæt hiː kʊd ˈoʊnli tˈɛstɪfˌaɪ wɪð sˈɜːtənti ðætðə pɹˈɪnt wʌz lˈɛs ðɐn θɹˈiː dˈeɪz ˈoʊld.
+DUMMY1/LJ037-0234.wav|mɪsˈɛs mˈɛɹi bɹˈɑːk, ðə wˈaɪf əvə mɪkˈænɪk hˌuː wˈɜːkt æt ðə stˈeɪʃən, wʌz ðɛɹ æt ðə tˈaɪm ænd ʃiː sˈɔː ɐ wˈaɪt mˈeɪl,
+DUMMY1/LJ040-0002.wav|tʃˈæptɚ sˈɛvən. lˈiː hˈɑːɹvi ˈɑːswəld: bˈækɡɹaʊnd ænd pˈɑːsəbəl mˈoʊɾɪvz, pˈɑːɹt wˌʌn.
+DUMMY1/LJ045-0140.wav|ðɪ ˈɑːɹɡjuːmənts hiː jˈuːzd tə dʒˈʌstɪfˌaɪ hɪz jˈuːs ʌvðɪ ˈeɪliəs sədʒˈɛst ðæt ˈɑːswəld mˌeɪhɐv kˈʌm tə θˈɪŋk ðætðə hˈoʊl wˈɜːld wʌz bɪkˈʌmɪŋ ɪnvˈɑːlvd
+DUMMY1/LJ012-0035.wav|ðə nˈʌmbɚ ænd nˈeɪmz ˌɑːn wˈɑːtʃᵻz, wɜː kˈɛɹfəli ɹɪmˈuːvd ɔːɹ əblˈɪɾɚɹˌeɪɾᵻd ˈæftɚ ðə ɡˈʊdz pˈæst ˌaʊɾəv hɪz hˈændz.
+DUMMY1/LJ012-0250.wav|ɑːnðə sˈɛvənθ dʒuːlˈaɪ, eɪtˈiːn θˈɜːɾisˈɛvən,
+DUMMY1/LJ016-0179.wav|kəntɹˈæktᵻd wɪð ʃˈɛɹɪfs ænd kənvˈɛnɚz tə wˈɜːk baɪ ðə dʒˈɑːb.
+DUMMY1/LJ016-0138.wav|æɾə dˈɪstəns fɹʌmðə pɹˈɪzən.
+DUMMY1/LJ027-0052.wav|ðiːz pɹˈɪnsɪpəlz ʌv həmˈɑːlədʒi ɑːɹ ɪsˈɛnʃəl tʊ ɐ kɚɹˈɛkt ɪntˌɜːpɹɪtˈeɪʃən ʌvðə fˈækts ʌv mɔːɹfˈɑːlədʒi.
+DUMMY1/LJ031-0134.wav|ˌɑːn wˈʌn əkˈeɪʒən mɪsˈɛs dʒˈɑːnsən, ɐkˈʌmpənɪd baɪ tˈuː sˈiːkɹət sˈɜːvɪs ˈeɪdʒənts, lˈɛft ðə ɹˈuːm tə sˈiː mɪsˈɛs kˈɛnədi ænd mɪsˈɛs kənˈæli.
+DUMMY1/LJ019-0273.wav|wˌɪtʃ sˌɜː dʒˈɑːʃjuːə dʒˈɛb tˈoʊld ðə kəmˈɪɾi hiː kənsˈɪdɚd ðə pɹˈɑːpɚɹ ˈɛlɪmənts ʌv pˈiːnəl dˈɪsɪplˌɪn.
+DUMMY1/LJ014-0110.wav|æt ðə fˈɜːst ðə bˈɑːksᵻz wɜːɹ ɪmpˈaʊndᵻd, ˈoʊpənd, ænd fˈaʊnd tə kəntˈeɪn mˈɛnɪəv oʊkˈɑːnɚz ɪfˈɛkts.
+DUMMY1/LJ034-0160.wav|ˌɑːn bɹˈɛnənz sˈʌbsɪkwənt sˈɜːtən aɪdˈɛntɪfɪkˈeɪʃən ʌv lˈiː hˈɑːɹvi ˈɑːswəld æz ðə mˈæn hiː sˈɔː fˈaɪɚ ðə ɹˈaɪfəl.
+DUMMY1/LJ038-0199.wav|ɪlˈɛvən. ɪf ˈaɪ æm ɐlˈaɪv ænd tˈeɪkən pɹˈɪzənɚ,
+DUMMY1/LJ014-0010.wav|jˈɛt hiː kʊd nˌɑːt ˌoʊvɚkˈʌm ðə stɹˈeɪndʒ fˌæsᵻnˈeɪʃən ɪt hˈɐd fɔːɹ hˌɪm, ænd ɹɪmˈeɪnd baɪ ðə sˈaɪd ʌvðə kˈɔːɹps tˈɪl ðə stɹˈɛtʃɚ kˈeɪm.
+DUMMY1/LJ033-0047.wav|ˈaɪ nˈoʊɾɪsd wɛn ˈaɪ wɛnt ˈaʊt ðætðə lˈaɪt wʌz ˈɑːn, ˈɛnd kwˈoʊt,
+DUMMY1/LJ040-0027.wav|hiː wʌz nˈɛvɚ sˈæɾɪsfˌaɪd wɪð ˈɛnɪθˌɪŋ.
+DUMMY1/LJ048-0228.wav|ænd ˈʌðɚz hˌuː wɜː pɹˈɛzənt sˈeɪ ðæt nˈoʊ ˈeɪdʒənt wʌz ɪnˈiːbɹɪˌeɪɾᵻd ɔːɹ ˈæktᵻd ɪmpɹˈɑːpɚli.
+DUMMY1/LJ003-0111.wav|hiː wʌz ɪn kˈɑːnsɪkwəns pˌʊt ˌaʊɾəv ðə pɹətˈɛkʃən ʌv ðɛɹ ɪntˈɜːnəl lˈɔː, ˈɛnd kwˈoʊt. ðɛɹ kˈoʊd wʌzɐ sˈʌbdʒɛkt ʌv sˌʌm kjˌʊɹɪˈɑːsɪɾi.
+DUMMY1/LJ008-0258.wav|lˈɛt mˌiː ɹɪtɹˈeɪs maɪ stˈɛps, ænd spˈiːk mˈoːɹ ɪn diːtˈeɪl ʌvðə tɹˈiːtmənt ʌvðə kəndˈɛmd ɪn ðoʊz blˈʌdθɜːsti ænd bɹˈuːɾəli ɪndˈɪfɹənt dˈeɪz,
+DUMMY1/LJ029-0022.wav|ðɪ ɚɹˈɪdʒɪnəl plˈæn kˈɔːld fɚðə pɹˈɛzɪdənt tə spˈɛnd ˈoʊnli wˈʌn dˈeɪ ɪnðə stˈeɪt, mˌeɪkɪŋ wˈɜːlwɪnd vˈɪzɪts tə dˈæləs, fˈɔːɹt wˈɜːθ, sˌæn æntˈoʊnɪˌoʊ, ænd hjˈuːstən.
+DUMMY1/LJ004-0045.wav|mˈɪstɚ stˈɜːdʒᵻz bˈoːɹn, sˌɜː dʒˈeɪmz mˈækɪntˌɑːʃ, sˌɜː dʒˈeɪmz skˈɑːɹlɪt, ænd wˈɪljəm wˈɪlbɚfˌoːɹs.

vits/filelists/vctk_audio_sid_text_test_filelist.txt

@@ -0,0 +1,500 @@
+DUMMY2/p229/p229_128.wav|67|The whole process is a vicious circle at the moment.
+DUMMY2/p234/p234_112.wav|3|That would be a serious problem.
+DUMMY2/p298/p298_125.wav|68|I asked why he had come.
+DUMMY2/p283/p283_318.wav|95|If not, he should go home.
+DUMMY2/p260/p260_046.wav|81|It is marvellous.
+DUMMY2/p281/p281_306.wav|36|These figures are truly awful.
+DUMMY2/p285/p285_247.wav|2|Now, suddenly, we have this new landscape.
+DUMMY2/p237/p237_180.wav|61|A helpline number is published at the end of this article.
+DUMMY2/p259/p259_052.wav|7|Maybe full-time referees will provide the answer.)
+DUMMY2/p314/p314_053.wav|51|Rangers deserved to beat us.
+DUMMY2/p345/p345_070.wav|82|I haven't made any definite decisions.
+DUMMY2/p269/p269_132.wav|94|Who will attend?
+DUMMY2/p347/p347_295.wav|46|It is typical of me.
+DUMMY2/p251/p251_223.wav|9|For the refugees, the return will not come a moment too soon.
+DUMMY2/p300/p300_224.wav|102|There is nothing like this back home.
+DUMMY2/p276/p276_076.wav|106|He confirmed that the document was valid.
+DUMMY2/p294/p294_271.wav|104|I also thought, This is a feature film.
+DUMMY2/p259/p259_257.wav|7|The amount of alcohol as a whole was very high.)
+DUMMY2/p248/p248_131.wav|99|The whole thing of doing the movie was a risk.
+DUMMY2/p334/p334_023.wav|38|If the red of the second bow falls upon the green of the first, the result is to give a bow with an abnormally wide yellow band, since red and green light when mixed form yellow.
+DUMMY2/p345/p345_386.wav|82|It is quite simple.
+DUMMY2/p330/p330_382.wav|1|Neither was involved in violence.
+DUMMY2/p246/p246_133.wav|5|My daughter is an adult.
+DUMMY2/p257/p257_140.wav|105|It's not true.
+DUMMY2/p340/p340_011.wav|74|When a man looks for something beyond his reach, his friends say he is looking for the pot of gold at the end of the rainbow.
+DUMMY2/p284/p284_409.wav|16|Then , he laughs.
+DUMMY2/p317/p317_129.wav|97|You would be wrong.
+DUMMY2/p279/p279_183.wav|25|Government will intervene.
+DUMMY2/p376/p376_273.wav|71|"If not, he should go home."
+DUMMY2/p233/p233_109.wav|84|It is not affected by the sale.
+DUMMY2/p234/p234_118.wav|3|When we looked at the company.
+DUMMY2/p336/p336_207.wav|98|The train was on time.
+DUMMY2/p227/p227_213.wav|29|What are you not good at ?
+DUMMY2/p347/p347_113.wav|46|They are all Arabs.
+DUMMY2/p317/p317_125.wav|97|Alan Milburn, the health secretary, refused to comment.
+DUMMY2/p341/p341_031.wav|66|I was left-handed, but it was just a matter of practice.
+DUMMY2/p244/p244_338.wav|78|This isn't a betrayal of public services, it's their renewal.
+DUMMY2/p250/p250_288.wav|24|Is it in the right place ?
+DUMMY2/p233/p233_156.wav|84|It opens the door to the Champions League.
+DUMMY2/p334/p334_118.wav|38|The sanctions are about collective punishment.
+DUMMY2/p258/p258_027.wav|26|People come into the Borders for the beauty of the background.
+DUMMY2/p341/p341_187.wav|66|His signature is his handwriting.
+DUMMY2/p258/p258_347.wav|26|The composer will conduct.
+DUMMY2/p262/p262_005.wav|45|She can scoop these things into three red bags, and we will go meet her Wednesday at the train station.
+DUMMY2/p231/p231_174.wav|50|One season, they might do well.
+DUMMY2/p363/p363_285.wav|6|But he was far from alone.
+DUMMY2/p303/p303_113.wav|44|Winning, meanwhile, is headed back to New York City.
+DUMMY2/p274/p274_181.wav|32|Is it in the right place ?)
+DUMMY2/p297/p297_023.wav|42|If the red of the second bow falls upon the green of the first, the result is to give a bow with an abnormally wide yellow band, since red and green light when mixed form yellow.
+DUMMY2/p247/p247_065.wav|14|We will pay their bills.)
+DUMMY2/p273/p273_105.wav|56|The pressure is on them.
+DUMMY2/p245/p245_167.wav|59|It was an odd affair, in many respects.
+DUMMY2/p364/p364_239.wav|88|It was a long time coming.
+DUMMY2/p263/p263_047.wav|39|The Yugoslav president said he did not recognise the election outcome.
+DUMMY2/p283/p283_333.wav|95|No final decision has been taken.
+DUMMY2/p335/p335_313.wav|49|The issues are very intense.
+DUMMY2/p280/p280_172.wav|52|He said some things which were better left alone.
+DUMMY2/p266/p266_006.wav|20|When the sunlight strikes raindrops in the air, they act as a prism and form a rainbow.
+DUMMY2/p260/p260_027.wav|81|Is this accurate?
+DUMMY2/p326/p326_214.wav|28|It's not long enough.
+DUMMY2/p259/p259_253.wav|7|You are like an animal.)
+DUMMY2/p228/p228_109.wav|57|However, the intensive care unit at the Southern General Hospital was full.
+DUMMY2/p376/p376_228.wav|71|"Half of young people had had contact with the police."
+DUMMY2/p361/p361_057.wav|79|That was something else.
+DUMMY2/p341/p341_058.wav|66|Labour accused the Tory leader of panicking.
+DUMMY2/p363/p363_247.wav|6|We are taking no chances this time.
+DUMMY2/p262/p262_054.wav|45|Already, he has been a tremendous influence in the dressing room.
+DUMMY2/p238/p238_090.wav|37|He thought she was amazing.
+DUMMY2/p306/p306_020.wav|12|Many complicated ideas about the rainbow have been formed.
+DUMMY2/p238/p238_339.wav|37|Harry Potter has lost his magic.
+DUMMY2/p302/p302_285.wav|30|Others said they had been beaten by police.
+DUMMY2/p275/p275_377.wav|40|Family liaison officers are now working to support the family.
+DUMMY2/p267/p267_286.wav|0|And they were being paid ?
+DUMMY2/p243/p243_090.wav|53|Among them was Gary Robertson from Dundee.
+DUMMY2/p274/p274_213.wav|32|It's easy to be negative about these things.)
+DUMMY2/p286/p286_310.wav|63|But it has been an amazing experience.
+DUMMY2/p294/p294_293.wav|104|That case has still not been settled.
+DUMMY2/p273/p273_174.wav|56|Two years later, she was dead.
+DUMMY2/p231/p231_408.wav|50|I should think so, too.
+DUMMY2/p323/p323_084.wav|34|And, within itself, it is visionary.
+DUMMY2/p248/p248_025.wav|99|She is given a new deputy minister for transport and planning.
+DUMMY2/p288/p288_197.wav|47|They are in the euro.
+DUMMY2/p300/p300_029.wav|102|Of course, this is nice to hear.
+DUMMY2/p299/p299_344.wav|58|He will never walk the streets again.
+DUMMY2/p376/p376_168.wav|71|"They will do their own thing."
+DUMMY2/p275/p275_277.wav|40|He looked very sharp.
+DUMMY2/p312/p312_022.wav|62|The actual primary rainbow observed is said to be the effect of super-imposition of a number of bows.
+DUMMY2/p278/p278_093.wav|10|It was some time before she found out he was safe.
+DUMMY2/p302/p302_312.wav|30|However, there was no hope, and glory too, for Scotland.
+DUMMY2/p236/p236_368.wav|75|It was like a weekly wage.
+DUMMY2/p237/p237_056.wav|61|No-one has appeared in court in relation to her death.
+DUMMY2/p305/p305_162.wav|54|For starters, many of the Scotland team didn't turn up.
+DUMMY2/p275/p275_018.wav|40|Aristotle thought that the rainbow was caused by reflection of the sun's rays by the rain.
+DUMMY2/p310/p310_039.wav|17|But one shouldn't go by that.
+DUMMY2/p299/p299_310.wav|58|Farmers have been an endangered species.
+DUMMY2/p259/p259_428.wav|7|In general terms, the proposals are very much in line with expectations.)
+DUMMY2/p339/p339_155.wav|18|It is just a matter of time.
+DUMMY2/p229/p229_347.wav|67|I've got no secret.
+DUMMY2/p256/p256_308.wav|90|Let's hope it's an investment in the future.
+DUMMY2/p360/p360_204.wav|60|It is dangerous and it is a lie.
+DUMMY2/p238/p238_208.wav|37|The refund is fully justified.
+DUMMY2/p341/p341_319.wav|66|This is very bad news.
+DUMMY2/p336/p336_399.wav|98|The pressure is enormous.
+DUMMY2/p229/p229_067.wav|67|That was the easy election.
+DUMMY2/p329/p329_159.wav|103|No-one, not even the Scottish Arts Council, was interested in her.
+DUMMY2/p258/p258_304.wav|26|The confidence is low, but it is a difficult thing to understand.
+DUMMY2/p312/p312_033.wav|62|Haven't been so lucky since.
+DUMMY2/p266/p266_093.wav|20|Two other men, including the taxi driver, were wounded in the attack.
+DUMMY2/p307/p307_396.wav|22|It was early morning.
+DUMMY2/p326/p326_039.wav|28|There is nothing like this back home.
+DUMMY2/p333/p333_009.wav|64|There is , according to legend, a boiling pot of gold at one end.
+DUMMY2/p295/p295_154.wav|92|I remember it clearly.
+DUMMY2/p297/p297_007.wav|42|The rainbow is a division of white light into many beautiful colors.
+DUMMY2/p233/p233_153.wav|84|It would be a last resort.
+DUMMY2/p244/p244_220.wav|78|This year, it will amount to a few hundred thousand pounds.
+DUMMY2/p267/p267_136.wav|0|It has become a way of life.
+DUMMY2/p311/p311_313.wav|4|The decision was left entirely to him.
+DUMMY2/p230/p230_113.wav|35|You can spend money on housing.
+DUMMY2/p318/p318_295.wav|19|We gave them the goal.
+DUMMY2/p236/p236_090.wav|75|After the match, do you ?
+DUMMY2/p364/p364_156.wav|88|Ferguson had done his homework.
+DUMMY2/p310/p310_260.wav|17|That is my role.
+DUMMY2/p323/p323_261.wav|34|They hoped to remain in the Edinburgh area.
+DUMMY2/p284/p284_393.wav|16|On the contrary, it was actually very funny.
+DUMMY2/p276/p276_460.wav|106|We will pay their bills.
+DUMMY2/p363/p363_273.wav|6|The plot is minimal.
+DUMMY2/p250/p250_039.wav|24|Costs have got to be controlled.
+DUMMY2/p317/p317_244.wav|97|This event allows us to emphasise the positive.
+DUMMY2/p280/p280_042.wav|52|He does not even trust his own members.
+DUMMY2/p227/p227_342.wav|29|Have a look at this lot.
+DUMMY2/p333/p333_255.wav|64|But the Foreign Secretary can cope.
+DUMMY2/p232/p232_103.wav|96|We recognise the important role of golf in attracting visitors.
+DUMMY2/p305/p305_138.wav|54|Another suggested the company should carry only pedestrians.
+DUMMY2/p248/p248_196.wav|99|It is not satisfied with the standard of fire safety provisions.
+DUMMY2/p230/p230_166.wav|35|We believe in the medium term.
+DUMMY2/p303/p303_275.wav|44|There are lots of these women in Finland.
+DUMMY2/p280/p280_208.wav|52|Gas production was also at record levels last year.
+DUMMY2/p330/p330_252.wav|1|I first met him last summer.
+DUMMY2/p330/p330_209.wav|1|I will not take you out of context.
+DUMMY2/p240/p240_214.wav|93|It had all been arranged.
+DUMMY2/p293/p293_185.wav|23|This is the stuff of live music.
+DUMMY2/p237/p237_230.wav|61|Clearly, the stakes are high.
+DUMMY2/p277/p277_014.wav|89|To the Hebrews it was a token that there would be no more universal floods.
+DUMMY2/p251/p251_107.wav|9|It had been played at festivals.
+DUMMY2/p302/p302_011.wav|30|When a man looks for something beyond his reach, his friends say he's looking for the pot of gold at the end of the rainbow.
+DUMMY2/p264/p264_147.wav|65|She's been shot.)
+DUMMY2/p236/p236_288.wav|75|Brown is an interesting man, but he is not desperate.
+DUMMY2/p323/p323_297.wav|34|I've got the shirt.
+DUMMY2/p297/p297_402.wav|42|I don't have a problem with getting older.
+DUMMY2/p267/p267_182.wav|0|A team is a team.
+DUMMY2/p226/p226_121.wav|43|Maybe this battle has been.
+DUMMY2/p311/p311_226.wav|4|Gone with them is any sense of narrative.
+DUMMY2/p335/p335_279.wav|49|Or rather he did and he didn't.
+DUMMY2/p270/p270_068.wav|8|Then followed a bout of flu.
+DUMMY2/p260/p260_072.wav|81|It was magic.
+DUMMY2/p362/p362_341.wav|15|The result could be all down to turnout.
+DUMMY2/p228/p228_180.wav|57|One season, they might do well.
+DUMMY2/p316/p316_152.wav|85|Failure is not an option.
+DUMMY2/p317/p317_423.wav|97|Manchester United are the classic example.
+DUMMY2/p243/p243_292.wav|53|Its work includes dealing with child abuse.
+DUMMY2/p362/p362_054.wav|15|We certainly hope we have been successful.
+DUMMY2/p243/p243_305.wav|53|What happened in that game ?
+DUMMY2/p364/p364_297.wav|88|It was just one man.
+DUMMY2/p255/p255_049.wav|31|We were surprised to see the photograph.
+DUMMY2/p297/p297_358.wav|42|He said he had no reports of casualties.
+DUMMY2/p283/p283_430.wav|95|My aim is a top six finish.
+DUMMY2/p310/p310_300.wav|17|Mike Tyson went to prison.
+DUMMY2/p363/p363_051.wav|6|The nation has his music.
+DUMMY2/p261/p261_112.wav|100|It became a national network.
+DUMMY2/p234/p234_036.wav|3|It was sold at a loss.
+DUMMY2/p247/p247_470.wav|14|They were good years for him.)
+DUMMY2/p303/p303_269.wav|44|After that nothing could save him.
+DUMMY2/p317/p317_256.wav|97|The man was pronounced dead on arrival.
+DUMMY2/p351/p351_161.wav|33|Paterson can afford to be generous.
+DUMMY2/p314/p314_295.wav|51|You take a risk.
+DUMMY2/p293/p293_268.wav|23|Our children are our future.
+DUMMY2/p306/p306_352.wav|12|Who has the second highest?
+DUMMY2/p273/p273_098.wav|56|The following are the principal provisions.
+DUMMY2/p285/p285_029.wav|2|Their courage, and their honesty, should be respected.
+DUMMY2/p266/p266_073.wav|20|It works for us.
+DUMMY2/p374/p374_288.wav|11|I had a good life at Rangers.
+DUMMY2/p280/p280_171.wav|52|You need a long-term strategy in football.
+DUMMY2/p239/p239_203.wav|48|It is all to do with the coaching.
+DUMMY2/p287/p287_292.wav|77|In essence, the teaching profession has a choice.
+DUMMY2/p330/p330_112.wav|1|Wallace was in at the deep end.
+DUMMY2/p247/p247_141.wav|14|They made such decisions in London.)
+DUMMY2/p277/p277_050.wav|89|This represents a tough game for us.
+DUMMY2/p233/p233_289.wav|84|He looked very sharp.
+DUMMY2/p284/p284_103.wav|16|Meanwhile, the Scottish Consumer Council yesterday offered support for the new Bill.
+DUMMY2/p334/p334_366.wav|38|We will miss him very much.
+DUMMY2/p238/p238_196.wav|37|Tiger is not the norm.
+DUMMY2/p304/p304_193.wav|72|Then they were awarded a penalty.
+DUMMY2/p229/p229_348.wav|67|Look at the witnesses.
+DUMMY2/p268/p268_147.wav|87|how do you get it back ?
+DUMMY2/p293/p293_348.wav|23|He quit in October.
+DUMMY2/p341/p341_082.wav|66|John Reid, the Northern Ireland secretary, yesterday appealed for restraint.
+DUMMY2/p258/p258_097.wav|26|It is a good lifestyle.
+DUMMY2/p340/p340_220.wav|74|Not so, it seems.
+DUMMY2/p269/p269_174.wav|94|Mark Fisher was a guest of the Northern Ireland Tourist Board.
+DUMMY2/p270/p270_078.wav|8|I've had it for the exams.
+DUMMY2/p334/p334_224.wav|38|I can't blame the fans.
+DUMMY2/p307/p307_306.wav|22|We're talking about creating an attractive neighbourhood.
+DUMMY2/p361/p361_205.wav|79|Translation - we got it wrong.
+DUMMY2/p229/p229_142.wav|67|What will happen then ?
+DUMMY2/p310/p310_221.wav|17|We will look into it.
+DUMMY2/p232/p232_357.wav|96|He had played well in that central role.
+DUMMY2/p263/p263_389.wav|39|This season has been a nightmare.
+DUMMY2/p283/p283_273.wav|95|Did he trip ?
+DUMMY2/p374/p374_277.wav|11|Where do you start?
+DUMMY2/p301/p301_289.wav|91|Children are using books in a terrible condition.
+DUMMY2/p345/p345_267.wav|82|Her presence was almost everywhere.
+DUMMY2/p264/p264_226.wav|65|No partners would lose their jobs.)
+DUMMY2/p253/p253_050.wav|70|A neighbour said.
+DUMMY2/p276/p276_118.wav|106|If they liked it then I'll be happy.
+DUMMY2/p295/p295_175.wav|92|Anything that can be done, the Government will do.
+DUMMY2/p247/p247_466.wav|14|I think it's a great system.)
+DUMMY2/p301/p301_182.wav|91|I see social work as a vocation, a commitment.
+DUMMY2/p294/p294_156.wav|104|We are well insured.
+DUMMY2/p287/p287_190.wav|77|We have to recognise that he is an elusive character.
+DUMMY2/p258/p258_333.wav|26|Robert is a special talent.
+DUMMY2/p275/p275_122.wav|40|Who would have?
+DUMMY2/p231/p231_259.wav|50|It was the climax of the thing.
+DUMMY2/p330/p330_073.wav|1|Over time, with patience and precision, the terrorists will be pursued.
+DUMMY2/p277/p277_239.wav|89|I should think so, too.
+DUMMY2/p374/p374_352.wav|11|As if they ever stopped.
+DUMMY2/p244/p244_258.wav|78|If it doesn't, it doesn't.
+DUMMY2/p277/p277_194.wav|89|I would think about the end of January, the beginning of February.
+DUMMY2/p241/p241_177.wav|86|The clarity is vital.
+DUMMY2/p247/p247_275.wav|14|What form did that take ?)
+DUMMY2/p230/p230_230.wav|35|That has been the easy part.
+DUMMY2/p323/p323_015.wav|34|The Greeks used to imagine that it was a sign from the gods to foretell war or heavy rain.
+DUMMY2/p269/p269_365.wav|94|But the real problem is the closure of the export market.
+DUMMY2/p310/p310_049.wav|17|They had four children together.
+DUMMY2/p281/p281_068.wav|36|I have proved that in the past.
+DUMMY2/p343/p343_162.wav|21|Dancing was her life.
+DUMMY2/p299/p299_208.wav|58|I'm a bit annoyed.
+DUMMY2/p329/p329_292.wav|103|The methadone programme is completely out of control.
+DUMMY2/p232/p232_376.wav|96|He could make it.
+DUMMY2/p305/p305_135.wav|54|I COULD hardly keep up with Professor McKean.
+DUMMY2/p351/p351_231.wav|33|We are pursuing legal action against the government.
+DUMMY2/p265/p265_153.wav|73|Military action is the only option we have on the table today.
+DUMMY2/p323/p323_137.wav|34|Everything was a dead end.
+DUMMY2/p305/p305_176.wav|54|That has given me great confidence.
+DUMMY2/p238/p238_053.wav|37|Does it matter ?
+DUMMY2/p230/p230_195.wav|35|It is not all good news and relief for Labour, however.
+DUMMY2/p238/p238_093.wav|37|He seems to have everything.
+DUMMY2/p259/p259_323.wav|7|We feel very comfortable in this international environment.)
+DUMMY2/p285/p285_032.wav|2|This is the window.
+DUMMY2/p302/p302_208.wav|30|Which means it matters.
+DUMMY2/p231/p231_176.wav|50|This much I can tell you.
+DUMMY2/p301/p301_054.wav|91|Here he is, in effect, appointing himself a judge.
+DUMMY2/p310/p310_102.wav|17|We shall rely on human beings.
+DUMMY2/p305/p305_121.wav|54|It is a vicious circle.
+DUMMY2/p231/p231_458.wav|50|She has reached the top of her profession.
+DUMMY2/p311/p311_024.wav|4|This is a very common type of bow, one showing mainly red and yellow, with little or no green or blue.
+DUMMY2/p245/p245_248.wav|59|Is there on his hands?
+DUMMY2/p333/p333_311.wav|64|Councillor Gordon has refused to stand down.
+DUMMY2/p299/p299_007.wav|58|The rainbow is a division of white light into many beautiful colors.
+DUMMY2/p229/p229_333.wav|67|She did not attend the courtroom.
+DUMMY2/p307/p307_286.wav|22|The board would report to the Scottish Parliament.
+DUMMY2/p305/p305_414.wav|54|It is the Holiday programme with a mortgage.
+DUMMY2/p264/p264_140.wav|65|He nearly killed my son.)
+DUMMY2/p374/p374_114.wav|11|I don't think it would make any difference.
+DUMMY2/p363/p363_369.wav|6|We have been overwhelmed by the response.
+DUMMY2/p293/p293_374.wav|23|I don't think the referees are against us.
+DUMMY2/p316/p316_329.wav|85|It's a production company.
+DUMMY2/p236/p236_018.wav|75|Aristotle thought that the rainbow was caused by reflection of the sun's rays by the rain.
+DUMMY2/p234/p234_332.wav|3|But it can be done.
+DUMMY2/p277/p277_132.wav|89|No production was achieved.
+DUMMY2/p326/p326_205.wav|28|As agreed, the prime minister was driven to Westminster Hall.
+DUMMY2/p272/p272_134.wav|69|This tour is critical for New Zealand rugby.
+DUMMY2/p316/p316_125.wav|85|And now the pressure is off.
+DUMMY2/p274/p274_149.wav|32|I prefer the clarity of the existing system.)
+DUMMY2/p227/p227_368.wav|29|A crucial moment has arrived.
+DUMMY2/p334/p334_206.wav|38|We'll have to work hard today.
+DUMMY2/p339/p339_087.wav|18|I am not completely insane.
+DUMMY2/p286/p286_453.wav|63|He was said to be emotionally disturbed.
+DUMMY2/p301/p301_110.wav|91|People want to see me on the screen.
+DUMMY2/p282/p282_188.wav|83|Suddenly, the rugby world had changed.
+DUMMY2/p263/p263_147.wav|39|Losing in that manner is very hard to take.
+DUMMY2/p256/p256_253.wav|90|I was never going to play against Scotland.
+DUMMY2/p374/p374_165.wav|11|Something has got to change.
+DUMMY2/p262/p262_232.wav|45|It's very safe.
+DUMMY2/p267/p267_417.wav|0|This is no reflection on Rangers.
+DUMMY2/p240/p240_078.wav|93|I've got the shirt.
+DUMMY2/p347/p347_143.wav|46|There is no sign of anyone being hurt.
+DUMMY2/p245/p245_069.wav|59|She died in hospital two hours later.
+DUMMY2/p233/p233_172.wav|84|They say that vital evidence was not heard in court.
+DUMMY2/p280/p280_282.wav|52|Overall, the last hole was good to the women.
+DUMMY2/p298/p298_364.wav|68|I'm looking at ways to do that now.
+DUMMY2/p339/p339_240.wav|18|The teacher would have approved.
+DUMMY2/p361/p361_387.wav|79|We have been going for three years.
+DUMMY2/p278/p278_221.wav|10|January is a bad time of year.
+DUMMY2/p334/p334_289.wav|38|They married in August last year.
+DUMMY2/p250/p250_187.wav|24|This championship is different from the other majors.
+DUMMY2/p248/p248_283.wav|99|Maloney is an engaging talent.
+DUMMY2/p275/p275_261.wav|40|It will be done in stages.
+DUMMY2/p288/p288_024.wav|47|This is a very common type of bow, one showing mainly red and yellow, with little or no green or blue.
+DUMMY2/p271/p271_454.wav|27|It's a miracle.
+DUMMY2/p252/p252_408.wav|55|They had to have hospital treatment.
+DUMMY2/p261/p261_192.wav|100|It was a pre-emptive strike.
+DUMMY2/p308/p308_099.wav|107|Glasgow deserved their win, but we made them look good.
+DUMMY2/p288/p288_070.wav|47|Neither it is.
+DUMMY2/p317/p317_356.wav|97|We're not an employment agency.
+DUMMY2/p351/p351_251.wav|33|That is a matter for the Scottish Parliament.
+DUMMY2/p329/p329_075.wav|103|This will be no easy option.
+DUMMY2/p261/p261_180.wav|100|I've been in two finals, and I've got a medal.
+DUMMY2/p301/p301_272.wav|91|Drink and petrol prices remain untouched.
+DUMMY2/p277/p277_404.wav|89|Whether the High Court will interfere with the sentence is another matter.
+DUMMY2/p301/p301_135.wav|91|How good is Lennox Lewis?
+DUMMY2/p246/p246_333.wav|5|Two people were interviewed.
+DUMMY2/p340/p340_250.wav|74|The film was great.
+DUMMY2/p268/p268_355.wav|87|They had declined in each of the two preceding quarters.
+DUMMY2/p236/p236_143.wav|75|The whole industry is a shambles.
+DUMMY2/p231/p231_398.wav|50|They have failed to deliver.
+DUMMY2/p340/p340_322.wav|74|I am extremely cautious.
+DUMMY2/p228/p228_048.wav|57|The Scottish Parliament is also looking at similar measures.
+DUMMY2/p334/p334_193.wav|38|It is in our own hands.
+DUMMY2/p226/p226_128.wav|43|I felt very strongly that England should have it.
+DUMMY2/p279/p279_064.wav|25|We have not given up hope.
+DUMMY2/p304/p304_416.wav|72|He took over our lives.
+DUMMY2/p313/p313_119.wav|76|O Neill is reputed to have replied.
+DUMMY2/p287/p287_195.wav|77|It comes from reflection or thinking.
+DUMMY2/p234/p234_008.wav|3|These take the shape of a long round arch, with its path high above, and its two ends apparently beyond the horizon.
+DUMMY2/p277/p277_119.wav|89|Naturally, it was not difficult to find support for these proposals.
+DUMMY2/p281/p281_394.wav|36|What are they for ?
+DUMMY2/p287/p287_272.wav|77|And they were being paid ?
+DUMMY2/p288/p288_071.wav|47|He seemed to lose his focus.
+DUMMY2/p335/p335_245.wav|49|A friendship that will endure.
+DUMMY2/p239/p239_061.wav|48|All manner of precaution and protection are taken.
+DUMMY2/p254/p254_003.wav|41|Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother Bob.
+DUMMY2/p259/p259_282.wav|7|Washington is consumed by the crisis.)
+DUMMY2/p253/p253_202.wav|70|Sadly, it can't.
+DUMMY2/p318/p318_333.wav|19|But when we do it is great.
+DUMMY2/p351/p351_363.wav|33|You will never forget the clutching horror.
+DUMMY2/p241/p241_374.wav|86|There is no signature.
+DUMMY2/p272/p272_216.wav|69|The report is due out next month.
+DUMMY2/p330/p330_355.wav|1|I've got my own ideas.
+DUMMY2/p270/p270_179.wav|8|The outcome is now in our own hands.
+DUMMY2/p257/p257_079.wav|105|It is not long term, but I need time to recover.
+DUMMY2/p257/p257_027.wav|105|They should have a major rethink about the event for next year.
+DUMMY2/p279/p279_118.wav|25|Does this mean.
+DUMMY2/p334/p334_058.wav|38|Hopefully, it will be built by next year.
+DUMMY2/p363/p363_178.wav|6|That was a huge experience.
+DUMMY2/p376/p376_227.wav|71|"And thought we would get away with it."
+DUMMY2/p330/p330_411.wav|1|You know, he was struggling with his game all week.
+DUMMY2/p326/p326_316.wav|28|It certainly sounded it at times.
+DUMMY2/p323/p323_048.wav|34|Mackie was at home, unable to watch.
+DUMMY2/p313/p313_422.wav|76|Now, that is a good deal.
+DUMMY2/p364/p364_113.wav|88|It was just great.
+DUMMY2/p286/p286_414.wav|63|It's just a training thing.
+DUMMY2/p288/p288_229.wav|47|However, no further action was taken by police.
+DUMMY2/p259/p259_142.wav|7|What happened in that game ?)
+DUMMY2/p297/p297_118.wav|42|It's too big a risk to take.
+DUMMY2/p313/p313_209.wav|76|The night is young.
+DUMMY2/p303/p303_279.wav|44|I bought a car at auction.
+DUMMY2/p345/p345_166.wav|82|Miller was every bit as happy.
+DUMMY2/p333/p333_289.wav|64|It's going to be quite a challenge.
+DUMMY2/p336/p336_323.wav|98|One paper was not returned.
+DUMMY2/p271/p271_082.wav|27|He is in the queue.
+DUMMY2/p314/p314_175.wav|51|There is no substitute.
+DUMMY2/p248/p248_124.wav|99|I can't even get into the A team.
+DUMMY2/p297/p297_160.wav|42|Tax is a matter for national governments.
+DUMMY2/p236/p236_299.wav|75|how do you get it back ?
+DUMMY2/p248/p248_300.wav|99|It wasn't just the character and energy of the playing.
+DUMMY2/p231/p231_429.wav|50|He is on the wrong side.
+DUMMY2/p250/p250_368.wav|24|We put our bid in last night.
+DUMMY2/p376/p376_191.wav|71|"I am totally surprised."
+DUMMY2/p250/p250_419.wav|24|She started to put on weight.
+DUMMY2/p239/p239_037.wav|48|He works at the airport.
+DUMMY2/p340/p340_165.wav|74|He was very fit.
+DUMMY2/p339/p339_258.wav|18|There are not too many like him.
+DUMMY2/p326/p326_266.wav|28|It may also be her last.
+DUMMY2/p231/p231_472.wav|50|He felt it was the right time.
+DUMMY2/p261/p261_411.wav|100|I had a fortunate war.
+DUMMY2/p272/p272_359.wav|69|Now, though, he has an incentive.
+DUMMY2/p340/p340_015.wav|74|The Greeks used to imagine that it was a sign from the gods to foretell war or heavy rain.
+DUMMY2/p283/p283_022.wav|95|The actual primary rainbow observed is said to be the effect of super-imposition of a number of bows.
+DUMMY2/p281/p281_334.wav|36|However, the groups denied the claims.
+DUMMY2/p318/p318_223.wav|19|We remain committed to it, as does the government.
+DUMMY2/p281/p281_039.wav|36|This film will be totally awesome.
+DUMMY2/p270/p270_013.wav|8|Some have accepted it as a miracle without physical explanation.
+DUMMY2/p243/p243_047.wav|53|However, there is an issue, isn't there ?
+DUMMY2/p374/p374_122.wav|11|The course is in great condition.
+DUMMY2/p302/p302_040.wav|30|On fuel, the Chancellor has a number of options.
+DUMMY2/p254/p254_231.wav|41|And thought we would get away with it.
+DUMMY2/p246/p246_222.wav|5|It's not before time.
+DUMMY2/p262/p262_044.wav|45|It is difficult for Ali.
+DUMMY2/p270/p270_005.wav|8|She can scoop these things into three red bags, and we will go meet her Wednesday at the train station.
+DUMMY2/p274/p274_340.wav|32|This is a historic occasion.)
+DUMMY2/p329/p329_045.wav|103|I hope you will leave it at that.
+DUMMY2/p285/p285_188.wav|2|Any change would be subject to the Scottish Parliament's approval.
+DUMMY2/p260/p260_193.wav|81|The Shadow Chancellor is away on holiday.
+DUMMY2/p259/p259_371.wav|7|He was unable to come.)
+DUMMY2/p275/p275_052.wav|40|Several other pupils and staff were seriously injured in the accident.
+DUMMY2/p233/p233_159.wav|84|But he stressed that the partnership is not a construction company.
+DUMMY2/p277/p277_312.wav|89|It will work.
+DUMMY2/p295/p295_211.wav|92|Leaving the Labour Party is one thing.
+DUMMY2/p297/p297_150.wav|42|It is the wealthiest in Europe.
+DUMMY2/p305/p305_026.wav|54|He added, however, that all options are under review.
+DUMMY2/p292/p292_121.wav|13|This would not be my first choice.
+DUMMY2/p253/p253_346.wav|70|It is the Holiday programme with a mortgage.
+DUMMY2/p363/p363_171.wav|6|He didn't know where to look.
+DUMMY2/p233/p233_128.wav|84|It is still too early for any likely contenders to have emerged.
+DUMMY2/p251/p251_137.wav|9|We are currently consulting with a wide range of interested parties.
+DUMMY2/p334/p334_034.wav|38|Appointed general secretary last September.
+DUMMY2/p286/p286_225.wav|63|This will take several weeks.
+DUMMY2/p363/p363_183.wav|6|Public safety is paramount.
+DUMMY2/p256/p256_207.wav|90|After that time, the market itself will set the prices.
+DUMMY2/p273/p273_311.wav|56|Job losses were also announced.
+DUMMY2/p274/p274_425.wav|32|The projections are very positive for South Africa.)
+DUMMY2/p254/p254_065.wav|41|That's the day job.
+DUMMY2/p335/p335_123.wav|49|Wagner was never like this.
+DUMMY2/p258/p258_105.wav|26|We do not expect any surplus.
+DUMMY2/p286/p286_294.wav|63|It was an easy decision to come here.
+DUMMY2/p361/p361_218.wav|79|But we were wrong.
+DUMMY2/p247/p247_426.wav|14|Being captain of this club is fantastic.)
+DUMMY2/p266/p266_391.wav|20|In time, may prove a worthy successor to Billy Dodds.
+DUMMY2/p253/p253_116.wav|70|It is so sad.
+DUMMY2/p261/p261_081.wav|100|Our mother is very worried.
+DUMMY2/p268/p268_131.wav|87|But then they scored their fourth.
+DUMMY2/p229/p229_192.wav|67|I have the first six months of next season to prove myself.
+DUMMY2/p275/p275_260.wav|40|They want to shut the Scottish Office.
+DUMMY2/p313/p313_109.wav|76|Nothing is being offered in exchange.
+DUMMY2/p347/p347_072.wav|46|Thankfully, Mr Campbell was able to help.
+DUMMY2/p298/p298_334.wav|68|Hopefully, the whole of Scottish rugby was paying attention.
+DUMMY2/p271/p271_232.wav|27|Jim Wallace, the justice minister, acknowledged that prisoner numbers were a concern.
+DUMMY2/p283/p283_056.wav|95|For the meantime, though, the signs are good.
+DUMMY2/p255/p255_239.wav|31|It's the same as Glasgow.
+DUMMY2/p267/p267_244.wav|0|We have come a long way in the last few sessions.
+DUMMY2/p340/p340_403.wav|74|I had a ball today.
+DUMMY2/p230/p230_083.wav|35|It might change your life.
+DUMMY2/p299/p299_403.wav|58|We will have to see, but it makes you think.
+DUMMY2/p343/p343_128.wav|21|Then came the crunch.
+DUMMY2/p297/p297_021.wav|42|The difference in the rainbow depends considerably upon the size of the drops, and the width of the colored band increases as the size of the drops increases.
+DUMMY2/p298/p298_275.wav|68|There are lots of these women in Finland.
+DUMMY2/p347/p347_286.wav|46|It is just too long since the war.
+DUMMY2/p239/p239_445.wav|48|Either group is living in fantasy land.
+DUMMY2/p286/p286_003.wav|63|Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother Bob.
+DUMMY2/p299/p299_082.wav|58|I still feel like a wee boy.
+DUMMY2/p306/p306_213.wav|12|It is like being a qualifier again.
+DUMMY2/p339/p339_305.wav|18|We know the goals will come.
+DUMMY2/p265/p265_274.wav|73|This time, for Rangers, it is certainly the latter.
+DUMMY2/p310/p310_382.wav|17|It has the Bank of Scotland behind it.
+DUMMY2/p335/p335_403.wav|49|Anyway, even if they didn't it wouldn't have mattered.
+DUMMY2/p246/p246_330.wav|5|I would be quite happy for the money to be given back.
+DUMMY2/p288/p288_386.wav|47|There is a solution, she believes.
+DUMMY2/p234/p234_019.wav|3|Since then physicists have found that it is not reflection, but refraction by the raindrops which causes the rainbows.
+DUMMY2/p287/p287_408.wav|77|FIRST, we had the Battle of Britain.
+DUMMY2/p286/p286_249.wav|63|She will attend in July.
+DUMMY2/p251/p251_235.wav|9|I'd never seen a play about me.
+DUMMY2/p347/p347_291.wav|46|Insurance will be covered by the receiving galleries.
+DUMMY2/p257/p257_058.wav|105|It is not great art.
+DUMMY2/p231/p231_471.wav|50|Dennis was not so sure.
+DUMMY2/p341/p341_107.wav|66|There was great support all round the route.
+DUMMY2/p264/p264_160.wav|65|It was clearly not a battle.)
+DUMMY2/p252/p252_155.wav|55|I think, therefore I am ?
+DUMMY2/p336/p336_264.wav|98|Ferguson must take the blame.
+DUMMY2/p274/p274_142.wav|32|The referee faces a massive job.)
+DUMMY2/p303/p303_005.wav|44|She can scoop these things into three red bags, and we will go meet her Wednesday at the train station.
+DUMMY2/p233/p233_240.wav|84|The singer is expected to be in hospital for several days.
+DUMMY2/p333/p333_220.wav|64|This process of attrition is expected to continue.
+DUMMY2/p285/p285_303.wav|2|Alex Smith has been a massive influence on my career as well.
+DUMMY2/p277/p277_348.wav|89|To do so he reckons that a good opening result is essential.
+DUMMY2/p311/p311_290.wav|4|I am not in denial.
+DUMMY2/p286/p286_316.wav|63|I am a retailer by nature.
+DUMMY2/p306/p306_119.wav|12|Completion is expected by October the following year.
+DUMMY2/p240/p240_028.wav|93|Is this accurate?
+DUMMY2/p238/p238_295.wav|37|It has been recorded twice.
+DUMMY2/p278/p278_049.wav|10|He will need that machine.
+DUMMY2/p351/p351_282.wav|33|We just wish they had done so before.
+DUMMY2/p267/p267_348.wav|0|Many of these properties are located in the south of England.
+DUMMY2/p312/p312_360.wav|62|And Scotland is no different.
+DUMMY2/p311/p311_324.wav|4|He pretended not to care.
+DUMMY2/p283/p283_389.wav|95|Scrutiny by the European Parliament is limited.
+DUMMY2/p266/p266_079.wav|20|He is in the queue.
+DUMMY2/p274/p274_424.wav|32|Not that Scotland can claim the moral high ground.)
+DUMMY2/p303/p303_169.wav|44|For athletes in our current climate, their sport is their livelihood.
+DUMMY2/p252/p252_237.wav|55|You know the type.
+DUMMY2/p323/p323_115.wav|34|Parts of the system are already overstretched.
+DUMMY2/p361/p361_013.wav|79|Some have accepted it as a miracle without physical explanation.
+DUMMY2/p333/p333_356.wav|64|Which he can do.
+DUMMY2/p241/p241_029.wav|86|However, the following year the cancer returned.
+DUMMY2/p248/p248_371.wav|99|Whether his stance is shared by the incoming manager is another matter.
+DUMMY2/p260/p260_007.wav|81|The rainbow is a division of white light into many beautiful colors.
+DUMMY2/p287/p287_257.wav|77|The concerns are the same.
+DUMMY2/p263/p263_125.wav|39|It isn't a happy memory.
+DUMMY2/p277/p277_258.wav|89|Immediate action must be taken.
+DUMMY2/p363/p363_219.wav|6|It was important in training terms.
+DUMMY2/p269/p269_191.wav|94|My main concern is that public health is not put at risk.
+DUMMY2/p262/p262_020.wav|45|Many complicated ideas about the rainbow have been formed.
+DUMMY2/p273/p273_023.wav|56|If the red of the second bow falls upon the green of the first, the result is to give a bow with an abnormally wide yellow band, since red and green light when mixed form yellow.
+DUMMY2/p278/p278_029.wav|10|They have now been banned from Celtic Park for life.
+DUMMY2/p310/p310_065.wav|17|I have had no social life at all.
+DUMMY2/p255/p255_352.wav|31|He's very explosive.
+DUMMY2/p376/p376_019.wav|71|"Since then physicists have found that it is not reflection, but refraction by the raindrops which causes the rainbows. "
+DUMMY2/p263/p263_307.wav|39|Is there a waiting list ?
+DUMMY2/p249/p249_258.wav|80|They must play for each other.
+DUMMY2/p258/p258_111.wav|26|Maybe this battle has been.
+DUMMY2/p316/p316_129.wav|85|There can be no compromise on that demand.

vits/filelists/vctk_audio_sid_text_test_filelist.txt.cleaned

@@ -0,0 +1,500 @@
+DUMMY2/p229/p229_128.wav|67|ðə hˈoʊl pɹˈɑːsɛs ɪz ɐ vˈɪʃəs sˈɜːkəl æt ðə mˈoʊmənt.
+DUMMY2/p234/p234_112.wav|3|ðæt wʊd biː ɐ sˈiəɹɪəs pɹˈɑːbləm.
+DUMMY2/p298/p298_125.wav|68|ˈaɪ ˈæskt wˌaɪ hiː hɐd kˈʌm.
+DUMMY2/p283/p283_318.wav|95|ɪf nˈɑːt, hiː ʃˌʊd ɡˌoʊ hˈoʊm.
+DUMMY2/p260/p260_046.wav|81|ɪt ɪz mˈɑːɹvələs.
+DUMMY2/p281/p281_306.wav|36|ðiːz fˈɪɡjɚz ɑːɹ tɹˈuːli ˈɔːfəl.
+DUMMY2/p285/p285_247.wav|2|nˈaʊ, sˈʌdənli, wiː hæv ðɪs nˈuː lˈændskeɪp.
+DUMMY2/p237/p237_180.wav|61|ɐ hˈɛlplaɪn nˈʌmbɚɹ ɪz pˈʌblɪʃt æt ðɪ ˈɛnd ʌv ðɪs ˈɑːɹɾɪkəl.
+DUMMY2/p259/p259_052.wav|7|mˈeɪbiː fˈʊltˈaɪm ɹˌɛfɚɹˈiːz wɪl pɹəvˈaɪd ðɪ ˈænsɚ.
+DUMMY2/p314/p314_053.wav|51|ɹˈeɪndʒɚz dɪzˈɜːvd tə bˈiːt ˌʌs.
+DUMMY2/p345/p345_070.wav|82|ˈaɪ hˈævənt mˌeɪd ˌɛni dˈɛfɪnət dᵻsˈɪʒənz.
+DUMMY2/p269/p269_132.wav|94|hˌuː wɪl ɐtˈɛnd?
+DUMMY2/p347/p347_295.wav|46|ɪt ɪz tˈɪpɪkəl ʌv mˌiː.
+DUMMY2/p251/p251_223.wav|9|fɚðə ɹˈɛfjuːdʒˌiːz, ðə ɹɪtˈɜːn wɪl nˌɑːt kˈʌm ɐ mˈoʊmənt tˈuː sˈuːn.
+DUMMY2/p300/p300_224.wav|102|ðɛɹ ɪz nˈʌθɪŋ lˈaɪk ðɪs bˈæk hˈoʊm.
+DUMMY2/p276/p276_076.wav|106|hiː kənfˈɜːmd ðætðə dˈɑːkjuːmənt wʌz vˈælɪd.
+DUMMY2/p294/p294_271.wav|104|ˈaɪ ˈɑːlsoʊ θˈɔːt, ðɪs ɪz ɐ fˈiːtʃɚ fˈɪlm.
+DUMMY2/p259/p259_257.wav|7|ðɪ ɐmˈaʊnt ʌv ˈælkəhˌɑːl æz ɐ hˈoʊl wʌz vˈɛɹi hˈaɪ.
+DUMMY2/p248/p248_131.wav|99|ðə hˈoʊl θˈɪŋ ʌv dˌuːɪŋ ðə mˈuːvi wʌzɐ ɹˈɪsk.
+DUMMY2/p334/p334_023.wav|38|ɪf ðə ɹˈɛd ʌvðə sˈɛkənd bˈoʊ fˈɔːlz əpˌɑːn ðə ɡɹˈiːn ʌvðə fˈɜːst, ðə ɹɪzˈʌlt ɪz tə ɡˈɪv ɐ bˈoʊ wɪð ɐn ɐbnˈoːɹməli wˈaɪd jˈɛloʊ bˈænd, sˈɪns ɹˈɛd ænd ɡɹˈiːn lˈaɪt wɛn mˈɪkst fˈɔːɹm jˈɛloʊ.
+DUMMY2/p345/p345_386.wav|82|ɪt ɪz kwˈaɪt sˈɪmpəl.
+DUMMY2/p330/p330_382.wav|1|nˈiːðɚ wʌz ɪnvˈɑːlvd ɪn vˈaɪələns.
+DUMMY2/p246/p246_133.wav|5|maɪ dˈɔːɾɚɹ ɪz ɐn ɐdˈʌlt.
+DUMMY2/p257/p257_140.wav|105|ɪts nˌɑːt tɹˈuː.
+DUMMY2/p340/p340_011.wav|74|wˌɛn ɐ mˈæn lˈʊks fɔːɹ sˈʌmθɪŋ bɪjˌɑːnd hɪz ɹˈiːtʃ, hɪz fɹˈɛndz sˈeɪ hiː ɪz lˈʊkɪŋ fɚðə pˈɑːt ʌv ɡˈoʊld æt ðɪ ˈɛnd ʌvðə ɹˈeɪnboʊ.
+DUMMY2/p284/p284_409.wav|16|ðˈɛn , hiː lˈæfz.
+DUMMY2/p317/p317_129.wav|97|juː wʊd biː ɹˈɔŋ.
+DUMMY2/p279/p279_183.wav|25|ɡˈʌvɚnmənt wɪl ˌɪntɚvˈiːn.
+DUMMY2/p376/p376_273.wav|71|"ɪf nˈɑːt, hiː ʃˌʊd ɡˌoʊ hˈoʊm."
+DUMMY2/p233/p233_109.wav|84|ɪt ɪz nˌɑːt ɐfˈɛktᵻd baɪ ðə sˈeɪl.
+DUMMY2/p234/p234_118.wav|3|wˌɛn wiː lˈʊkt æt ðə kˈʌmpəni.
+DUMMY2/p336/p336_207.wav|98|ðə tɹˈeɪn wʌz ˌɑːn tˈaɪm.
+DUMMY2/p227/p227_213.wav|29|wˌʌt ɑːɹ juː nˌɑːt ɡˈʊd æt ?
+DUMMY2/p347/p347_113.wav|46|ðeɪ ɑːɹ ˈɔːl ˈæɹəbz.
+DUMMY2/p317/p317_125.wav|97|ˈælən mˈɪlbɜːn, ðə hˈɛlθ sˈɛkɹətɹi, ɹɪfjˈuːzd tə kˈɑːmɛnt.
+DUMMY2/p341/p341_031.wav|66|ˈaɪ wʌz lˈɛfthˈændᵻd, bˌʌt ɪt wʌz dʒˈʌst ɐ mˈæɾɚɹ ʌv pɹˈæktɪs.
+DUMMY2/p244/p244_338.wav|78|ðɪs ˌɪzənt ɐ bɪtɹˈeɪəl ʌv pˈʌblɪk sˈɜːvɪsᵻz, ɪts ðɛɹ ɹɪnˈuːəl.
+DUMMY2/p250/p250_288.wav|24|ɪz ɪt ɪnðə ɹˈaɪt plˈeɪs ?
+DUMMY2/p233/p233_156.wav|84|ɪt ˈoʊpənz ðə dˈoːɹ tə ðə tʃˈæmpiənz lˈiːɡ.
+DUMMY2/p334/p334_118.wav|38|ðə sˈænkʃənz ɑːɹ ɐbˌaʊt kəlˈɛktɪv pˈʌnɪʃmənt.
+DUMMY2/p258/p258_027.wav|26|pˈiːpəl kˈʌm ˌɪntʊ ðə bˈoːɹdɚz fɚðə bjˈuːɾi ʌvðə bˈækɡɹaʊnd.
+DUMMY2/p341/p341_187.wav|66|hɪz sˈɪɡnɪtʃɚɹ ɪz hɪz hˈændɹaɪɾɪŋ.
+DUMMY2/p258/p258_347.wav|26|ðə kəmpˈoʊzɚ wɪl kəndˈʌkt.
+DUMMY2/p262/p262_005.wav|45|ʃiː kæn skˈuːp ðiːz θˈɪŋz ˌɪntʊ θɹˈiː ɹˈɛd bˈæɡz, ænd wiː wɪl ɡˌoʊ mˈiːt hɜː wˈɛnzdeɪ æt ðə tɹˈeɪn stˈeɪʃən.
+DUMMY2/p231/p231_174.wav|50|wˈʌn sˈiːzən, ðeɪ mˌaɪt dˈuː wˈɛl.
+DUMMY2/p363/p363_285.wav|6|bˌʌt hiː wʌz fˈɑːɹ fɹʌm ɐlˈoʊn.
+DUMMY2/p303/p303_113.wav|44|wˈɪnɪŋ, mˈiːnwaɪl, ɪz hˈɛdᵻd bˈæk tə nˈuː jˈɔːɹk sˈɪɾi.
+DUMMY2/p274/p274_181.wav|32|ɪz ɪt ɪnðə ɹˈaɪt plˈeɪs ?
+DUMMY2/p297/p297_023.wav|42|ɪf ðə ɹˈɛd ʌvðə sˈɛkənd bˈoʊ fˈɔːlz əpˌɑːn ðə ɡɹˈiːn ʌvðə fˈɜːst, ðə ɹɪzˈʌlt ɪz tə ɡˈɪv ɐ bˈoʊ wɪð ɐn ɐbnˈoːɹməli wˈaɪd jˈɛloʊ bˈænd, sˈɪns ɹˈɛd ænd ɡɹˈiːn lˈaɪt wɛn mˈɪkst fˈɔːɹm jˈɛloʊ.
+DUMMY2/p247/p247_065.wav|14|wiː wɪl pˈeɪ ðɛɹ bˈɪlz.
+DUMMY2/p273/p273_105.wav|56|ðə pɹˈɛʃɚɹ ɪz ˈɑːn ðˌɛm.
+DUMMY2/p245/p245_167.wav|59|ɪt wʌz ɐn ˈɑːd ɐfˈɛɹ, ɪn mˈɛni ɹɪspˈɛkts.
+DUMMY2/p364/p364_239.wav|88|ɪt wʌzɐ lˈɑːŋ tˈaɪm kˈʌmɪŋ.
+DUMMY2/p263/p263_047.wav|39|ðə jˈuːɡəslˌæv pɹˈɛzɪdənt sˈɛd hiː dɪdnˌɑːt ɹˈɛkəɡnˌaɪz ð�� ɪlˈɛkʃən ˈaʊtkʌm.
+DUMMY2/p283/p283_333.wav|95|nˈoʊ fˈaɪnəl dᵻsˈɪʒən hɐzbɪn tˈeɪkən.
+DUMMY2/p335/p335_313.wav|49|ðɪ ˈɪʃuːz ɑːɹ vˈɛɹi ɪntˈɛns.
+DUMMY2/p280/p280_172.wav|52|hiː sˈɛd sˌʌm θˈɪŋz wˌɪtʃ wɜː bˈɛɾɚ lˈɛft ɐlˈoʊn.
+DUMMY2/p266/p266_006.wav|20|wˌɛn ðə sˈʌnlaɪt stɹˈaɪks ɹˈeɪndɹɑːps ɪnðɪ ˈɛɹ, ðeɪ ˈækt æz ɐ pɹˈɪzəm ænd fˈɔːɹm ɐ ɹˈeɪnboʊ.
+DUMMY2/p260/p260_027.wav|81|ɪz ðɪs ˈækjʊɹət?
+DUMMY2/p326/p326_214.wav|28|ɪts nˌɑːt lˈɑːŋ ɪnˈʌf.
+DUMMY2/p259/p259_253.wav|7|juː ɑːɹ lˈaɪk ɐn ˈænɪməl.
+DUMMY2/p228/p228_109.wav|57|haʊˈɛvɚ, ðɪ ɪntˈɛnsɪv kˈɛɹ jˈuːnɪt æt ðə sˈʌðɚn dʒˈɛnɚɹəl hˈɑːspɪɾəl wʌz fˈʊl.
+DUMMY2/p376/p376_228.wav|71|"hˈæf ʌv jˈʌŋ pˈiːpəl hɐd hɐd kˈɑːntækt wɪððə pəlˈiːs."
+DUMMY2/p361/p361_057.wav|79|ðæt wʌz sˈʌmθɪŋ ˈɛls.
+DUMMY2/p341/p341_058.wav|66|lˈeɪbɚɹ ɐkjˈuːzd ðə tˈoːɹi lˈiːdɚɹ ʌv pˈænɪkɪŋ.
+DUMMY2/p363/p363_247.wav|6|wiː ɑːɹ tˈeɪkɪŋ nˈoʊ tʃˈænsᵻz ðɪs tˈaɪm.
+DUMMY2/p262/p262_054.wav|45|ɔːlɹˈɛdi, hiː hɐzbɪn ɐ tɹəmˈɛndəs ˈɪnfluːəns ɪnðə dɹˈɛsɪŋ ɹˈuːm.
+DUMMY2/p238/p238_090.wav|37|hiː θˈɔːt ʃiː wʌz ɐmˈeɪzɪŋ.
+DUMMY2/p306/p306_020.wav|12|mˈɛni kˈɑːmplᵻkˌeɪɾᵻd aɪdˈiəz ɐbˌaʊt ðə ɹˈeɪnboʊ hɐvbɪn fˈɔːɹmd.
+DUMMY2/p238/p238_339.wav|37|hˈæɹi pˈɑːɾɚ hɐz lˈɔst hɪz mˈædʒɪk.
+DUMMY2/p302/p302_285.wav|30|ˈʌðɚz sˈɛd ðeɪ hɐdbɪn bˈiːʔn̩ baɪ pəlˈiːs.
+DUMMY2/p275/p275_377.wav|40|fˈæmɪli liːˈeɪzɑːn ˈɑːfɪsɚz ɑːɹ nˈaʊ wˈɜːkɪŋ tə səpˈoːɹt ðə fˈæmɪli.
+DUMMY2/p267/p267_286.wav|0|ænd ðeɪ wɜː bˌiːɪŋ pˈeɪd ?
+DUMMY2/p243/p243_090.wav|53|ɐmˌʌŋ ðˌɛm wʌz ɡˈæɹi ɹˈɑːbɚtsən fɹʌm dˈʌndiː.
+DUMMY2/p274/p274_213.wav|32|ɪts ˈiːzi təbi nˈɛɡətˌɪv ɐbˌaʊt ðiːz θˈɪŋz.
+DUMMY2/p286/p286_310.wav|63|bˌʌt ɪt hɐzbɪn ɐn ɐmˈeɪzɪŋ ɛkspˈiəɹɪəns.
+DUMMY2/p294/p294_293.wav|104|ðæt kˈeɪs hɐz stˈɪl nˌɑːt bˌɪn sˈɛɾəld.
+DUMMY2/p273/p273_174.wav|56|tˈuː jˈɪɹz lˈeɪɾɚ, ʃiː wʌz dˈɛd.
+DUMMY2/p231/p231_408.wav|50|ˈaɪ ʃˌʊd θˈɪŋk sˈoʊ, tˈuː.
+DUMMY2/p323/p323_084.wav|34|ˈænd, wɪðˌɪn ɪtsˈɛlf, ɪt ɪz vˈɪʒənˌɛɹi.
+DUMMY2/p248/p248_025.wav|99|ʃiː ɪz ɡˈɪvən ɐ nˈuː dˈɛpjuːɾi mˈɪnɪstɚ fɔːɹ tɹˈænspoːɹt ænd plˈænɪŋ.
+DUMMY2/p288/p288_197.wav|47|ðeɪ ɑːɹ ɪnðə jˈʊɹɹoʊ.
+DUMMY2/p300/p300_029.wav|102|ʌv kˈoːɹs, ðɪs ɪz nˈaɪs tə hˈɪɹ.
+DUMMY2/p299/p299_344.wav|58|hiː wɪl nˈɛvɚ wˈɔːk ðə stɹˈiːts ɐɡˈɛn.
+DUMMY2/p376/p376_168.wav|71|"ðeɪ wɪl dˈuː ðɛɹ ˈoʊn θˈɪŋ."
+DUMMY2/p275/p275_277.wav|40|hiː lˈʊkt vˈɛɹi ʃˈɑːɹp.
+DUMMY2/p312/p312_022.wav|62|ðɪ ˈæktʃuːəl pɹˈaɪmɚɹi ɹˈeɪnboʊ ɑːbzˈɜːvd ɪz sˈɛd təbi ðɪ ɪfˈɛkt ʌv sˈuːpɚɹɪmpəzˈɪʃən əvə nˈʌmbɚɹ ʌv bˈoʊz.
+DUMMY2/p278/p278_093.wav|10|ɪt wʌz sˌʌm tˈaɪm bɪfˌoːɹ ʃiː fˈaʊnd ˈaʊt hiː wʌz sˈeɪf.
+DUMMY2/p302/p302_312.wav|30|haʊˈɛvɚ, ðɛɹwˌʌz nˈoʊ hˈoʊp, ænd ɡlˈoːɹi tˈuː, fɔːɹ skˈɑːtlənd.
+DUMMY2/p236/p236_368.wav|75|ɪt wʌz lˈaɪk ɐ wˈiːkli wˈeɪdʒ.
+DUMMY2/p237/p237_056.wav|61|nˈoʊwˈʌn hɐz ɐpˈɪɹd ɪn kˈoːɹt ɪn ɹɪlˈeɪʃən tə hɜː dˈɛθ.
+DUMMY2/p305/p305_162.wav|54|fɔːɹ stˈɑːɹɾɚz, mˈɛnɪəv ðə skˈɑːtlənd tˈiːm dˈɪdnt tˈɜːn ˈʌp.
+DUMMY2/p275/p275_018.wav|40|ˈæɹɪstˌɑːɾəl θˈɔːt ðætðə ɹˈeɪnboʊ wʌz kˈɔːzd baɪ ɹɪflˈɛkʃən ʌvðə sˈʌnz ɹˈeɪz baɪ ðə ɹˈeɪn.
+DUMMY2/p310/p310_039.wav|17|bˌʌt wˈʌn ʃˌʊdənt ɡˌoʊ baɪ ðˈæt.
+DUMMY2/p299/p299_310.wav|58|fˈɑːɹmɚz hɐvbɪn ɐn ɛndˈeɪndʒɚd spˈiːsiːz.
+DUMMY2/p259/p259_428.wav|7|ɪn dʒˈɛnɚɹəl tˈɜːmz, ðə pɹəpˈoʊzəlz ɑːɹ vˈɛɹi mˈʌtʃ ɪn lˈaɪn wɪð ɛkspɪktˈeɪʃənz.
+DUMMY2/p339/p339_155.wav|18|ɪt ɪz dʒˈʌst ɐ mˈæɾɚɹ ʌv tˈaɪm.
+DUMMY2/p229/p229_347.wav|67|aɪv ɡɑːt nˈoʊ sˈiːkɹət.
+DUMMY2/p256/p256_308.wav|90|lˈɛts hˈoʊp ɪts ɐn ɪnvˈɛstmənt ɪnðə fjˈuːtʃɚ.
+DUMMY2/p360/p360_204.wav|60|ɪt ɪz dˈeɪndʒɚɹəs ænd ɪt ɪz ɐ lˈaɪ.
+DUMMY2/p238/p238_208.wav|37|ðə ɹˈiːfʌnd ɪz fˈʊli dʒˈʌstɪfˌaɪd.
+DUMMY2/p341/p341_319.wav|66|ðɪs ɪz vˈɛɹi bˈæd nˈuːz.
+DUMMY2/p336/p336_399.wav|98|ðə pɹˈɛʃɚɹ ɪz ɪnˈoːɹməs.
+DUMMY2/p229/p229_067.wav|67|ðæt wʌzðɪ ˈiːzi ɪlˈɛkʃən.
+DUMMY2/p329/p329_159.wav|103|nˈoʊwˈʌn, nˌɑːt ˈiːvən ðə skˈɑːɾɪʃ ˈɑːɹts kˈaʊnsəl, wʌz ˈɪntɹəstᵻd ɪn hɜː.
+DUMMY2/p258/p258_304.wav|26|ðə kˈɑːnfɪdəns ɪz lˈoʊ, bˌʌt ɪt ɪz ɐ dˈɪfɪkəlt θˈɪŋ tʊ ˌʌndɚstˈænd.
+DUMMY2/p312/p312_033.wav|62|hˈævənt bˌɪn sˌoʊ lˈʌki sˈɪns.
+DUMMY2/p266/p266_093.wav|20|tˈuː ˈʌðɚ mˈɛn, ɪnklˈuːdɪŋ ðə tˈæksi dɹˈaɪvɚ, wɜː wˈuːndᵻd ɪnðɪ ɐtˈæk.
+DUMMY2/p307/p307_396.wav|22|ɪt wʌz ˈɜːli mˈɔːɹnɪŋ.
+DUMMY2/p326/p326_039.wav|28|ðɛɹ ɪz nˈʌθɪŋ lˈaɪk ðɪs bˈæk hˈoʊm.
+DUMMY2/p333/p333_009.wav|64|ðɛɹˈɪz , ɐkˈoːɹdɪŋ tə lˈɛdʒənd, ɐ bˈɔɪlɪŋ pˈɑːt ʌv ɡˈoʊld æt wˈʌn ˈɛnd.
+DUMMY2/p295/p295_154.wav|92|ˈaɪ ɹɪmˈɛmbɚɹ ɪt klˈɪɹli.
+DUMMY2/p297/p297_007.wav|42|ðə ɹˈeɪnboʊ ɪz ɐ dɪvˈɪʒən ʌv wˈaɪt lˈaɪt ˌɪntʊ mˈɛni bjˈuːɾɪfəl kˈʌlɚz.
+DUMMY2/p233/p233_153.wav|84|ɪt wʊd biː ɐ lˈæst ɹɪzˈɔːɹt.
+DUMMY2/p244/p244_220.wav|78|ðɪs jˈɪɹ, ɪt wɪl ɐmˈaʊnt tʊ ɐ fjˈuː hˈʌndɹəd θˈaʊzənd pˈaʊndz.
+DUMMY2/p267/p267_136.wav|0|ɪt hɐz bɪkˌʌm ɐ wˈeɪ ʌv lˈaɪf.
+DUMMY2/p311/p311_313.wav|4|ðə dᵻsˈɪʒən wʌz lˈɛft ɛntˈaɪɚli tə hˌɪm.
+DUMMY2/p230/p230_113.wav|35|juː kæn spˈɛnd mˈʌni ˌɑːn hˈaʊzɪŋ.
+DUMMY2/p318/p318_295.wav|19|wiː ɡˈeɪv ðˌɛm ðə ɡˈoʊl.
+DUMMY2/p236/p236_090.wav|75|ˈæftɚ ðə mˈætʃ, dˈuː juː ?
+DUMMY2/p364/p364_156.wav|88|fˈɜːɡəsən hɐd dˈʌn hɪz hˈoʊmwɜːk.
+DUMMY2/p310/p310_260.wav|17|ðæt ɪz maɪ ɹˈoʊl.
+DUMMY2/p323/p323_261.wav|34|ðeɪ hˈoʊpt tə ɹɪmˈeɪn ɪnðɪ ˈɛdɪnbʌɹə ˈɛɹiə.
+DUMMY2/p284/p284_393.wav|16|ɑːnðə kˈɑːntɹɛɹi, ɪt wʌz ˈæktʃuːəli vˈɛɹi fˈʌni.
+DUMMY2/p276/p276_460.wav|106|wiː wɪl pˈeɪ ðɛɹ bˈɪlz.
+DUMMY2/p363/p363_273.wav|6|ðə plˈɑːt ɪz mˈɪnɪməl.
+DUMMY2/p250/p250_039.wav|24|kˈɔsts hæv ɡɑːt təbi kəntɹˈoʊld.
+DUMMY2/p317/p317_244.wav|97|ðɪs ɪvˈɛnt ɐlˈaʊz ˌʌs tʊ ˈɛmfɐsˌaɪz ðə pˈɑːzɪtˌɪv.
+DUMMY2/p280/p280_042.wav|52|hiː dʌznˌɑːt ˈiːvən tɹˈʌst hɪz ˈoʊn mˈɛmbɚz.
+DUMMY2/p227/p227_342.wav|29|hæv ɐ lˈʊk æt ðɪs lˈɑːt.
+DUMMY2/p333/p333_255.wav|64|bˌʌt ðə fˈɔːɹən sˈɛkɹətɹi kæn kˈoʊp.
+DUMMY2/p232/p232_103.wav|96|wiː ɹˈɛkəɡnˌaɪz ðɪ ɪmpˈoːɹtənt ɹˈoʊl ʌv ɡˈɑːlf ɪn ɐtɹˈæktɪŋ vˈɪzɪɾɚz.
+DUMMY2/p305/p305_138.wav|54|ɐnˈʌðɚ sədʒˈɛstᵻd ðə kˈʌmpəni ʃˌʊd kˈæɹi ˈoʊnli pədˈɛstɹiənz.
+DUMMY2/p248/p248_196.wav|99|ɪt ɪz nˌɑːt sˈæɾɪsfˌaɪd wɪððə stˈændɚd ʌv fˈaɪɚ sˈeɪfti pɹəvˈɪʒənz.
+DUMMY2/p230/p230_166.wav|35|wiː bɪlˈiːv ɪnðə mˈiːdiəm tˈɜːm.
+DUMMY2/p303/p303_275.wav|44|ðɛɹˌɑːɹ lˈɑːts ʌv ðiːz wˈɪmɪn ɪn fˈɪnlənd.
+DUMMY2/p280/p280_208.wav|52|ɡˈæs pɹədˈʌkʃən wʌz ˈɑːlsoʊ æt ɹˈɛkɚd lˈɛvəlz lˈæst jˈɪɹ.
+DUMMY2/p330/p330_252.wav|1|ˈaɪ fˈɜːst mˈɛt hˌɪm lˈæst sˈʌmɚ.
+DUMMY2/p330/p330_209.wav|1|ˈaɪ wɪl nˌɑːt tˈeɪk juː ˌaʊɾəv kˈɑːntɛkst.
+DUMMY2/p240/p240_214.wav|93|ɪt hɐd ˈɔːl bˌɪn ɐɹˈeɪndʒd.
+DUMMY2/p293/p293_185.wav|23|ðɪs ɪz ðə stˈʌf ʌv lˈaɪv mjˈuːzɪk.
+DUMMY2/p237/p237_230.wav|61|klˈɪɹli, ðə stˈeɪks ɑːɹ hˈaɪ.
+DUMMY2/p277/p277_014.wav|89|tə ðə hˈiːbɹuːz ɪt wʌzɐ tˈoʊkən ðæt ðɛɹ wʊd biː nˈoʊmˌoːɹ jˌuːnɪvˈɜːsəl flˈʌdz.
+DUMMY2/p251/p251_107.wav|9|ɪt hɐdbɪn plˈeɪd æt fˈɛstɪvəlz.
+DUMMY2/p302/p302_011.wav|30|wˌɛn ɐ mˈæn lˈʊks fɔːɹ sˈʌmθɪŋ bɪjˌɑːnd hɪz ɹˈiːtʃ, hɪz fɹˈɛndz sˈeɪ hiːz lˈʊkɪŋ fɚðə pˈɑːt ʌv ɡˈoʊld æt ðɪ ˈɛnd ʌvðə ɹˈeɪnboʊ.
+DUMMY2/p264/p264_147.wav|65|ʃiːz bˌɪn ʃˈɑːt.
+DUMMY2/p236/p236_288.wav|75|bɹˈaʊn ɪz ɐn ˈɪntɹəstɪŋ mˈæn, bˌʌt hiː ɪz nˌɑːt dˈɛspɚɹət.
+DUMMY2/p323/p323_297.wav|34|aɪv ɡɑːt ðə ʃˈɜːt.
+DUMMY2/p297/p297_402.wav|42|ˈaɪ dˈoʊnt hæv ɐ pɹˈɑːbləm wɪð ɡˌɛɾɪŋ ˈoʊldɚ.
+DUMMY2/p267/p267_182.wav|0|ɐ tˈiːm ɪz ɐ tˈiːm.
+DUMMY2/p226/p226_121.wav|43|mˈeɪbiː ðɪs bˈæɾəl hɐzbɪn.
+DUMMY2/p311/p311_226.wav|4|ɡɔn wɪð ðˌɛm ɪz ˌɛni sˈɛns ʌv nˈæɹətˌɪv.
+DUMMY2/p335/p335_279.wav|49|ɔːɹ ɹˈæðɚ hiː dˈɪd ænd hiː dˈɪdnt.
+DUMMY2/p270/p270_068.wav|8|ðˈɛn fˈɑːloʊd ɐ bˈaʊt ʌv flˈuː.
+DUMMY2/p260/p260_072.wav|81|ɪt wʌz mˈædʒɪk.
+DUMMY2/p362/p362_341.wav|15|ðə ɹɪzˈʌlt kʊd biː ˈɔːl dˌaʊn tə tˈɜːnaʊt.
+DUMMY2/p228/p228_180.wav|57|wˈʌn sˈiːzən, ðeɪ mˌaɪt dˈuː wˈɛl.
+DUMMY2/p316/p316_152.wav|85|fˈeɪlɪɹ ɪz nˌɑːt ɐn ˈɑːpʃən.
+DUMMY2/p317/p317_423.wav|97|mˈæntʃɛstɚ juːnˈaɪɾᵻd ɑːɹ ðə klˈæsɪk ɛɡzˈæmpəl.
+DUMMY2/p243/p243_292.wav|53|ɪts wˈɜːk ɪnklˈuːdz dˈiːlɪŋ wɪð tʃˈaɪld ɐbjˈuːs.
+DUMMY2/p362/p362_054.wav|15|wiː sˈɜːtənli hˈoʊp wiː hɐvbɪn səksˈɛsfəl.
+DUMMY2/p243/p243_305.wav|53|wˌʌt hˈæpənd ɪn ðæt ɡˈeɪm ?
+DUMMY2/p364/p364_297.wav|88|ɪt wʌz dʒˈʌst wˈʌn mˈæn.
+DUMMY2/p255/p255_049.wav|31|wiː wɜː sɚpɹˈaɪzd tə sˈiː ðə fˈoʊɾəɡɹˌæf.
+DUMMY2/p297/p297_358.wav|42|hiː sˈɛd hiː hɐd nˈoʊ ɹɪpˈoːɹts ʌv kˈæʒuːəlɾɪz.
+DUMMY2/p283/p283_430.wav|95|maɪ ˈeɪm ɪz ɐ tˈɑːp sˈɪks fˈɪnɪʃ.
+DUMMY2/p310/p310_300.wav|17|mˈaɪk tˈaɪsən wɛnt tə pɹˈɪzən.
+DUMMY2/p363/p363_051.wav|6|ðə nˈeɪʃən hɐz hɪz mjˈuːzɪk.
+DUMMY2/p261/p261_112.wav|100|ɪt bɪkˌeɪm ɐ nˈæʃənəl nˈɛtwɜːk.
+DUMMY2/p234/p234_036.wav|3|ɪt wʌz sˈoʊld æɾə lˈɔs.
+DUMMY2/p247/p247_470.wav|14|ðeɪ wɜː ɡˈʊd jˈɪɹz fɔːɹ hˌɪm.
+DUMMY2/p303/p303_269.wav|44|ˈæftɚ ðæt nˈʌθɪŋ kʊd sˈeɪv hˌɪm.
+DUMMY2/p317/p317_256.wav|97|ðə mˈæn wʌz pɹənˈaʊnst dˈɛd ˌɑːn ɐɹˈaɪvəl.
+DUMMY2/p351/p351_161.wav|33|pˈæɾɚsən kæn ɐfˈoːɹd təbi dʒˈɛnɚɹəs.
+DUMMY2/p314/p314_295.wav|51|juː tˈeɪk ɐ ɹˈɪsk.
+DUMMY2/p293/p293_268.wav|23|ˌaʊɚ tʃˈɪldɹən ɑːɹ ˌaʊɚ fjˈuːtʃɚ.
+DUMMY2/p306/p306_352.wav|12|hˌuː hɐz ðə sˈɛkənd hˈaɪəst?
+DUMMY2/p273/p273_098.wav|56|ðə fˈɑːloʊɪŋ ɑːɹ ðə pɹˈɪnsɪpəl pɹəvˈɪʒənz.
+DUMMY2/p285/p285_029.wav|2|ðɛɹ kˈɜːɹɪdʒ, ænd ðɛɹ ˈɑːnɪsti, ʃˌʊd biː ɹɪspˈɛktᵻd.
+DUMMY2/p266/p266_073.wav|20|ɪt wˈɜːks fɔːɹ ˌʌs.
+DUMMY2/p374/p374_288.wav|11|ˈaɪ hɐd ɐ ɡˈʊd lˈaɪf æt ɹˈeɪndʒɚz.
+DUMMY2/p280/p280_171.wav|52|juː nˈiːd ɐ lˈɑːŋtˈɜːm stɹˈæɾədʒi ɪn fˈʊtbɔːl.
+DUMMY2/p239/p239_203.wav|48|ɪt ɪz ˈɔːl tə dˈuː wɪððə kˈoʊtʃɪŋ.
+DUMMY2/p287/p287_292.wav|77|ɪn ˈɛsəns, ðə tˈiːtʃɪŋ pɹəfˈɛʃən hɐz ɐ tʃˈɔɪs.
+DUMMY2/p330/p330_112.wav|1|wˈɑːlᵻs wʌz ɪn æt ðə dˈiːp ˈɛnd.
+DUMMY2/p247/p247_141.wav|14|ðeɪ mˌeɪd sˈʌtʃ dᵻsˈɪʒənz ɪn lˈʌndən.
+DUMMY2/p277/p277_050.wav|89|ðɪs ɹˌɛpɹɪzˈɛnts ɐ tˈʌf ɡˈeɪm fɔːɹ ˌʌs.
+DUMMY2/p233/p233_289.wav|84|hiː lˈʊkt vˈɛɹi ʃˈɑːɹp.
+DUMMY2/p284/p284_103.wav|16|mˈiːnwaɪl, ðə skˈɑːɾɪʃ kənsˈuːmɚ kˈaʊnsəl jˈɛstɚdˌeɪ ˈɑːfɚd səpˈoːɹt fɚðə nˈuː bˈɪl.
+DUMMY2/p334/p334_366.wav|38|wiː wɪl mˈɪs hˌɪm vˈɛɹi mˈʌtʃ.
+DUMMY2/p238/p238_196.wav|37|tˈaɪɡɚɹ ɪz nˌɑːt ðə nˈɔːɹm.
+DUMMY2/p304/p304_193.wav|72|ðˈɛn ðeɪ wɜːɹ ɐwˈɔːɹdᵻd ɐ pˈɛnəlɾi.
+DUMMY2/p229/p229_348.wav|67|lˈʊk æt ðə wˈɪtnəsᵻz.
+DUMMY2/p268/p268_147.wav|87|hˌaʊ dˈuː juː ɡɛt ɪt bˈæk ?
+DUMMY2/p293/p293_348.wav|23|hiː kwˈɪt ɪn ɑːktˈoʊbɚ.
+DUMMY2/p341/p341_082.wav|66|dʒˈɑːn ɹˈiːd, ðə nˈɔːɹðɚn ˈaɪɚlənd sˈɛkɹətɹi, jˈɛstɚdˌeɪ ɐpˈiːld fɔːɹ ɹɪstɹˈeɪnt.
+DUMMY2/p258/p258_097.wav|26|ɪt ɪz ɐ ɡˈʊd lˈaɪfstaɪl.
+DUMMY2/p340/p340_220.wav|74|nˌɑːt sˈoʊ, ɪt sˈiːmz.
+DUMMY2/p269/p269_174.wav|94|mˈɑːɹk fˈɪʃɚ wʌzɐ ɡˈɛst ʌvðə nˈɔːɹðɚn ˈaɪɚlənd tˈʊɹɪst bˈoːɹd.
+DUMMY2/p270/p270_078.wav|8|aɪvhˌæd ɪt fɚðɪ ɛɡzˈæmz.
+DUMMY2/p334/p334_224.wav|38|ˈaɪ kˈænt blˈeɪm ðə fˈænz.
+DUMMY2/p307/p307_306.wav|22|wɪɹ tˈɔːkɪŋ ɐbˌaʊt kɹiːˈeɪɾɪŋ ɐn ɐtɹˈæktɪv nˈeɪbɚhˌʊd.
+DUMMY2/p361/p361_205.wav|79|tɹænslˈeɪʃən wiː ɡɑːt ɪt ɹˈɔŋ.
+DUMMY2/p229/p229_142.wav|67|wˌʌt wɪl hˈæpən ðˈɛn ?
+DUMMY2/p310/p310_221.wav|17|wiː wɪl lˈʊk ˌɪntʊ ɪt.
+DUMMY2/p232/p232_357.wav|96|hiː hɐd plˈeɪd wˈɛl ɪn ðæt sˈɛntɹəl ɹˈoʊl.
+DUMMY2/p263/p263_389.wav|39|ðɪs sˈiːzən hɐzbɪn ɐ nˈaɪtmɛɹ.
+DUMMY2/p283/p283_273.wav|95|dˈɪd hiː tɹˈɪp ?
+DUMMY2/p374/p374_277.wav|11|wˌɛɹ dˈuː juː stˈɑːɹt?
+DUMMY2/p301/p301_289.wav|91|tʃˈɪldɹən ɑːɹ jˈuːzɪŋ bˈʊks ɪn ɐ tˈɛɹəbəl kəndˈɪʃən.
+DUMMY2/p345/p345_267.wav|82|hɜː pɹˈɛzəns wʌz ˈɔːlmoʊst ˈɛvɹɪwˌɛɹ.
+DUMMY2/p264/p264_226.wav|65|nˈoʊ pˈɑːɹtnɚz wʊd lˈuːz ðɛɹ dʒˈɑːbz.
+DUMMY2/p253/p253_050.wav|70|ɐ nˈeɪbɚ sˈɛd.
+DUMMY2/p276/p276_118.wav|106|ɪf ðeɪ lˈaɪkt ɪt ðˈɛn aɪl biː hˈæpi.
+DUMMY2/p295/p295_175.wav|92|ˈɛnɪθˌɪŋ ðæt kæn biː dˈʌn, ðə ɡˈʌvɚnmənt wɪl dˈuː.
+DUMMY2/p247/p247_466.wav|14|ˈaɪ θˈɪŋk ɪts ɐ ɡɹˈeɪt sˈɪstəm.
+DUMMY2/p301/p301_182.wav|91|ˈaɪ sˈiː sˈoʊʃəl wˈɜːk æz ɐ voʊkˈeɪʃən, ɐ kəmˈɪtmənt.
+DUMMY2/p294/p294_156.wav|104|wiː ɑːɹ wˈɛl ɪnʃˈʊɹd.
+DUMMY2/p287/p287_190.wav|77|wiː hæv tə ɹˈɛkəɡnˌaɪz ðæt hiː ɪz ɐn ɪlˈuːsɪv kˈæɹɪktɚ.
+DUMMY2/p258/p258_333.wav|26|ɹˈɑːbɚt ɪz ɐ spˈɛʃəl tˈælənt.
+DUMMY2/p275/p275_122.wav|40|hˌuː wˈʊdhæv?
+DUMMY2/p231/p231_259.wav|50|ɪt wʌzðə klˈaɪmæks ʌvðə θˈɪŋ.
+DUMMY2/p330/p330_073.wav|1|ˌoʊvɚ tˈaɪm, wɪð pˈeɪʃəns ænd pɹɪsˈɪʒən, ðə tˈɛɹɚɹˌɪsts wɪl biː pɚsˈuːd.
+DUMMY2/p277/p277_239.wav|89|ˈaɪ ʃˌʊd θˈɪŋk sˈoʊ, tˈuː.
+DUMMY2/p374/p374_352.wav|11|æz ɪf ðeɪ ˈɛvɚ stˈɑːpt.
+DUMMY2/p244/p244_258.wav|78|ɪf ɪt dˈʌzənt, ɪt dˈʌzənt.
+DUMMY2/p277/p277_194.wav|89|ˈaɪ wʊd θˈɪŋk ɐbˌaʊt ðɪ ˈɛnd ʌv dʒˈænjuːˌɛɹi, ðə bɪɡˈɪnɪŋ ʌv fˈɛbɹuːˌɛɹi.
+DUMMY2/p241/p241_177.wav|86|ðə klˈæɹɪɾi ɪz vˈaɪɾəl.
+DUMMY2/p247/p247_275.wav|14|wˌʌt fˈɔːɹm dˈɪd ðæt tˈeɪk ?
+DUMMY2/p230/p230_230.wav|35|ðɐthɐzbˌɪn ðɪ ˈiːzi pˈɑːɹt.
+DUMMY2/p323/p323_015.wav|34|ðə ��ɹˈiːks jˈuːzd tʊ ɪmˈædʒɪn ðˌɐɾɪt wʌzɐ sˈaɪn fɹʌmðə ɡˈɑːdz tə foːɹtˈɛl wˈɔːɹ ɔːɹ hˈɛvi ɹˈeɪn.
+DUMMY2/p269/p269_365.wav|94|bˌʌt ðə ɹˈiːəl pɹˈɑːbləm ɪz ðə klˈoʊʒɚɹ ʌvðɪ ˈɛkspoːɹt mˈɑːɹkɪt.
+DUMMY2/p310/p310_049.wav|17|ðeɪ hɐd fˈoːɹ tʃˈɪldɹən təɡˈɛðɚ.
+DUMMY2/p281/p281_068.wav|36|ˈaɪ hæv pɹˈuːvd ðæt ɪnðə pˈæst.
+DUMMY2/p343/p343_162.wav|21|dˈænsɪŋ wʌz hɜː lˈaɪf.
+DUMMY2/p299/p299_208.wav|58|aɪm ɐ bˈɪt ɐnˈɔɪd.
+DUMMY2/p329/p329_292.wav|103|ðə mˈɛθɐdˌoʊn pɹˈoʊɡɹæm ɪz kəmplˈiːtli ˌaʊɾəv kəntɹˈoʊl.
+DUMMY2/p232/p232_376.wav|96|hiː kʊd mˈeɪk ɪt.
+DUMMY2/p305/p305_135.wav|54|ˈaɪ kʊd hˈɑːɹdli kˈiːp ˌʌp wɪð pɹəfˈɛsɚ məkˈiːn.
+DUMMY2/p351/p351_231.wav|33|wiː ɑːɹ pɚsˈuːɪŋ lˈiːɡəl ˈækʃən ɐɡˈɛnst ðə ɡˈʌvɚnmənt.
+DUMMY2/p265/p265_153.wav|73|mˈɪlətˌɛɹi ˈækʃən ɪz ðɪ ˈoʊnli ˈɑːpʃən wiː hæv ɑːnðə tˈeɪbəl tədˈeɪ.
+DUMMY2/p323/p323_137.wav|34|ˈɛvɹɪθˌɪŋ wʌzɐ dˈɛd ˈɛnd.
+DUMMY2/p305/p305_176.wav|54|ðæt hɐz ɡˈɪvən mˌiː ɡɹˈeɪt kˈɑːnfɪdəns.
+DUMMY2/p238/p238_053.wav|37|dˈʌz ɪt mˈæɾɚ ?
+DUMMY2/p230/p230_195.wav|35|ɪt ɪz nˌɑːt ˈɔːl ɡˈʊd nˈuːz ænd ɹɪlˈiːf fɔːɹ lˈeɪbɚ, haʊˈɛvɚ.
+DUMMY2/p238/p238_093.wav|37|hiː sˈiːmz tə hæv ˈɛvɹɪθˌɪŋ.
+DUMMY2/p259/p259_323.wav|7|wiː fˈiːl vˈɛɹi kˈʌmftəbəl ɪn ðɪs ˌɪntɚnˈæʃənəl ɛnvˈaɪɹənmənt.
+DUMMY2/p285/p285_032.wav|2|ðɪs ɪz ðə wˈɪndoʊ.
+DUMMY2/p302/p302_208.wav|30|wˌɪtʃ mˈiːnz ɪt mˈæɾɚz.
+DUMMY2/p231/p231_176.wav|50|ðɪs mˈʌtʃ ˈaɪ kæn tˈɛl juː.
+DUMMY2/p301/p301_054.wav|91|hˈɪɹ hiː ɪz, ɪn ɪfˈɛkt, ɐpˈɔɪntɪŋ hɪmsˈɛlf ɐ dʒˈʌdʒ.
+DUMMY2/p310/p310_102.wav|17|wiːʃˌɐl ɹɪlˈaɪ ˌɑːn hjˈuːmən bˈiːɪŋz.
+DUMMY2/p305/p305_121.wav|54|ɪt ɪz ɐ vˈɪʃəs sˈɜːkəl.
+DUMMY2/p231/p231_458.wav|50|ʃiː hɐz ɹˈiːtʃt ðə tˈɑːp ʌv hɜː pɹəfˈɛʃən.
+DUMMY2/p311/p311_024.wav|4|ðɪs ɪz ɐ vˈɛɹi kˈɑːmən tˈaɪp ʌv bˈoʊ, wˈʌn ʃˈoʊɪŋ mˈeɪnli ɹˈɛd ænd jˈɛloʊ, wɪð lˈɪɾəl ɔːɹ nˈoʊ ɡɹˈiːn ɔːɹ blˈuː.
+DUMMY2/p245/p245_248.wav|59|ɪz ðɛɹ ˌɑːn hɪz hˈændz?
+DUMMY2/p333/p333_311.wav|64|kˈaʊnsɪlɚ ɡˈoːɹdən hɐz ɹɪfjˈuːzd tə stˈænd dˈaʊn.
+DUMMY2/p299/p299_007.wav|58|ðə ɹˈeɪnboʊ ɪz ɐ dɪvˈɪʒən ʌv wˈaɪt lˈaɪt ˌɪntʊ mˈɛni bjˈuːɾɪfəl kˈʌlɚz.
+DUMMY2/p229/p229_333.wav|67|ʃiː dɪdnˌɑːt ɐtˈɛnd ðə kˈoːɹtɹuːm.
+DUMMY2/p307/p307_286.wav|22|ðə bˈoːɹd wʊd ɹɪpˈoːɹt tə ðə skˈɑːɾɪʃ pˈɑːɹləmənt.
+DUMMY2/p305/p305_414.wav|54|ɪt ɪz ðə hˈɑːlɪdˌeɪ pɹˈoʊɡɹæm wɪð ɐ mˈɔːɹɡɪdʒ.
+DUMMY2/p264/p264_140.wav|65|hiː nˌɪɹli kˈɪld maɪ sˈʌn.
+DUMMY2/p374/p374_114.wav|11|ˈaɪ dˈoʊnt θˈɪŋk ɪt wʊd mˌeɪk ˌɛni dˈɪfɹəns.
+DUMMY2/p363/p363_369.wav|6|wiː hɐvbɪn ˌoʊvɚwˈɛlmd baɪ ðə ɹɪspˈɑːns.
+DUMMY2/p293/p293_374.wav|23|ˈaɪ dˈoʊnt θˈɪŋk ðə ɹˌɛfɚɹˈiːz ɑːɹ ɐɡˈɛnst ˌʌs.
+DUMMY2/p316/p316_329.wav|85|ɪts ɐ pɹədˈʌkʃən kˈʌmpəni.
+DUMMY2/p236/p236_018.wav|75|ˈæɹɪstˌɑːɾəl θˈɔːt ðætðə ɹˈeɪnboʊ wʌz kˈɔːzd baɪ ɹɪflˈɛkʃən ʌvðə sˈʌnz ɹˈeɪz baɪ ðə ɹˈeɪn.
+DUMMY2/p234/p234_332.wav|3|bˌʌt ɪt kæn biː dˈʌn.
+DUMMY2/p277/p277_132.wav|89|nˈoʊ pɹədˈʌkʃən wʌz ɐtʃˈiːvd.
+DUMMY2/p326/p326_205.wav|28|æz ɐɡɹˈiːd, ðə pɹˈaɪm mˈɪnɪstɚ wʌz dɹˈɪvən tə wˈɛstmɪnstɚ hˈɔːl.
+DUMMY2/p272/p272_134.wav|69|ðɪs tˈʊɹ ɪz kɹˈɪɾɪkəl fɔːɹ nˈuː zˈiːlənd ɹˈʌɡbi.
+DUMMY2/p316/p316_125.wav|85|ænd nˈaʊ ðə pɹˈɛʃɚɹ ɪz ˈɔf.
+DUMMY2/p274/p274_149.wav|32|ˈaɪ pɹɪfˈɜː ðə klˈæɹɪɾi ʌvðɪ ɛɡzˈɪstɪŋ sˈɪstəm.
+DUMMY2/p227/p227_368.wav|29|ɐ kɹˈuːʃəl mˈoʊmənt hɐz ɐɹˈaɪvd.
+DUMMY2/p334/p334_206.wav|38|wiːl hæv tə wˈɜːk hˈɑːɹd tədˈeɪ.
+DUMMY2/p339/p339_087.wav|18|ˈaɪ æm nˌɑːt kəmplˈiːtli ɪnsˈeɪn.
+DUMMY2/p286/p286_453.wav|63|hiː wʌz sˈɛd təbi ɪmˈoʊʃənəli dɪstˈɜːbd.
+DUMMY2/p301/p301_110.wav|91|pˈiːpəl wˈɑːnt tə sˈiː mˌiː ɑːnðə skɹˈiːn.
+DUMMY2/p282/p282_188.wav|83|sˈʌdənli, ðə ɹˈʌɡbi wˈɜːld hɐd tʃˈeɪndʒd.
+DUMMY2/p263/p263_147.wav|39|lˈuːzɪŋ ɪn ðæt mˈænɚɹ ɪz vˈɛɹi hˈɑːɹd tə tˈeɪk.
+DUMMY2/p256/p256_253.wav|90|ˈaɪ wʌz nˈɛvɚ ɡˌoʊɪŋ tə plˈeɪ ɐɡˈɛnst skˈɑːtlənd.
+DUMMY2/p374/p374_165.wav|11|sˈʌmθɪŋ hɐz ɡɑːt tə tʃˈeɪndʒ.
+DUMMY2/p262/p262_232.wav|45|ɪts vˈɛɹi sˈeɪf.
+DUMMY2/p267/p267_417.wav|0|ðɪs ɪz nˈoʊ ɹɪflˈɛkʃən ˌɑːn ɹˈeɪndʒɚz.
+DUMMY2/p240/p240_078.wav|93|aɪv ɡɑːt ðə ʃˈɜːt.
+DUMMY2/p347/p347_143.wav|46|ðɛɹ ɪz nˈoʊ sˈaɪn ʌv ˈɛnɪwˌʌn bˌiːɪŋ hˈɜːt.
+DUMMY2/p245/p245_069.wav|59|ʃiː dˈaɪd ɪn hˈɑːspɪɾəl tˈuː ˈaɪʊɹz lˈeɪɾɚ.
+DUMMY2/p233/p233_172.wav|84|ðeɪ sˈeɪ ðæt vˈaɪɾəl ˈɛvɪdəns wʌz nˌɑːt hˈɜːd ɪn kˈoːɹt.
+DUMMY2/p280/p280_282.wav|52|ˌoʊvɚɹˈɔːl, ðə lˈæst hˈoʊl wʌz ɡˈʊd tə ðə wˈɪmɪn.
+DUMMY2/p298/p298_364.wav|68|aɪm lˈʊkɪŋ æt wˈeɪz tə dˈuː ðæt nˈaʊ.
+DUMMY2/p339/p339_240.wav|18|ðə tˈiːtʃɚ wʊdhɐv ɐpɹˈuːvd.
+DUMMY2/p361/p361_387.wav|79|wiː hɐvbɪn ɡˌoʊɪŋ fɔːɹ θɹˈiː jˈɪɹz.
+DUMMY2/p278/p278_221.wav|10|dʒˈænjuːˌɛɹi ɪz ɐ bˈæd tˈaɪm ʌv jˈɪɹ.
+DUMMY2/p334/p334_289.wav|38|ðeɪ mˈæɹɪd ɪn ˈɔːɡəst lˈæst jˈɪɹ.
+DUMMY2/p250/p250_187.wav|24|ðɪs tʃˈæmpiənʃˌɪp ɪz dˈɪfɹənt fɹʌmðɪ ˈʌðɚ mˈeɪdʒɚz.
+DUMMY2/p248/p248_283.wav|99|mˈæloʊni ɪz ɐn ɛnɡˈeɪdʒɪŋ tˈælənt.
+DUMMY2/p275/p275_261.wav|40|ɪt wɪl biː dˈʌn ɪn stˈeɪdʒᵻz.
+DUMMY2/p288/p288_024.wav|47|ðɪs ɪz ɐ vˈɛɹi kˈɑːmən tˈaɪp ʌv bˈoʊ, wˈʌn ʃˈoʊɪŋ mˈeɪnli ɹˈɛd ænd jˈɛloʊ, wɪð lˈɪɾəl ɔːɹ nˈoʊ ɡɹˈiːn ɔːɹ blˈuː.
+DUMMY2/p271/p271_454.wav|27|ɪts ɐ mˈɪɹəkəl.
+DUMMY2/p252/p252_408.wav|55|ðeɪ hædtə hæv hˈɑːspɪɾəl tɹˈiːtmənt.
+DUMMY2/p261/p261_192.wav|100|ɪt wʌzɐ pɹˈiːˈɛmptɪv stɹˈaɪk.
+DUMMY2/p308/p308_099.wav|107|ɡlˈæzɡoʊ dɪzˈɜːvd ðɛɹ wˈɪn, bˌʌt wiː mˌeɪd ðˌɛm lˈʊk ɡˈʊd.
+DUMMY2/p288/p288_070.wav|47|nˈiːðɚɹ ɪt ˈɪz.
+DUMMY2/p317/p317_356.wav|97|wɪɹ nˌɑːt ɐn ɛmplˈɔɪmənt ˈeɪdʒənsi.
+DUMMY2/p351/p351_251.wav|33|ðæt ɪz ɐ mˈæɾɚ fɚðə skˈɑːɾɪʃ pˈɑːɹləmənt.
+DUMMY2/p329/p329_075.wav|103|ðɪs wɪl biː nˈoʊ ˈiːzi ˈɑːpʃən.
+DUMMY2/p261/p261_180.wav|100|aɪv bˌɪn ɪn tˈuː fˈaɪnəlz, ænd aɪv ɡɑːt ɐ mˈɛdəl.
+DUMMY2/p301/p301_272.wav|91|dɹˈɪŋk ænd pˈɛtɹəl pɹˈaɪsᵻz ɹɪmˈeɪn ʌntˈʌtʃt.
+DUMMY2/p277/p277_404.wav|89|wˈɛðɚ ðə hˈaɪ kˈoːɹt wɪl ˌɪntəfˈɪɹ wɪððə sˈɛntəns ɪz ɐnˈʌðɚ mˈæɾɚ.
+DUMMY2/p301/p301_135.wav|91|hˌaʊ ɡˈʊd ɪz lˈɛnɑːks lˈuːiz?
+DUMMY2/p246/p246_333.wav|5|tˈuː pˈiːpəl wɜːɹ ˈɪntɚvjˌuːd.
+DUMMY2/p340/p340_250.wav|74|ðə fˈɪlm wʌz ɡɹˈeɪt.
+DUMMY2/p268/p268_355.wav|87|ðeɪ hɐd dᵻklˈaɪnd ɪn ˈiːtʃ əv ðə tˈuː pɹɪsˈiːdɪŋ kwˈɔːɹɾɚz.
+DUMMY2/p236/p236_143.wav|75|ðə hˈoʊl ˈɪndʌstɹi ɪz ɐ ʃˈæmbəlz.
+DUMMY2/p231/p231_398.wav|50|ðeɪ hæv fˈeɪld tə dɪlˈɪvɚ.
+DUMMY2/p340/p340_322.wav|74|ˈaɪ æm ɛkstɹˈiːmli kˈɔːʃəs.
+DUMMY2/p228/p228_048.wav|57|ðə skˈɑːɾɪʃ pˈɑːɹləmənt ɪz ˈɑːlsoʊ lˈʊkɪŋ æt sˈɪmɪlɚ mˈɛʒɚz.
+DUMMY2/p334/p334_193.wav|38|ɪt ɪz ɪn ˌaʊɚɹ ˈoʊn hˈændz.
+DUMMY2/p226/p226_128.wav|43|ˈaɪ fˈɛlt vˈɛɹi stɹˈɔŋli ðæt ˈɪŋɡlənd ʃʊdhˈævɪt.
+DUMMY2/p279/p279_064.wav|25|wiː hɐvnˌɑːt ɡˈɪvən ˌʌp hˈoʊp.
+DUMMY2/p304/p304_416.wav|72|hiː tˈʊk ˌoʊvɚɹ ˌaʊɚ lˈaɪvz.
+DUMMY2/p313/p313_119.wav|76|ˈoʊ nˈiːl ɪz ɹɪpjˈuːɾᵻd tə hæv ɹɪplˈaɪd.
+DUMMY2/p287/p287_195.wav|77|ɪt kˈʌmz fɹʌm ɹɪflˈɛkʃən ɔːɹ θˈɪŋkɪŋ.
+DUMMY2/p234/p234_008.wav|3|ðiːz tˈeɪk ðə ʃˈeɪp əvə lˈɑːŋ ɹˈaʊnd ˈɑːɹtʃ, wɪð ɪts pˈæθ hˈaɪ əbˈʌv, ænd ɪts tˈuː ˈɛndz ɐpˈæɹəntli bɪjˌɑːnd ðə hɚɹˈaɪzən.
+DUMMY2/p277/p277_119.wav|89|nˈætʃɚɹəli, ɪt wʌz nˌɑːt dˈɪfɪkəlt tə fˈaɪnd səpˈoːɹt fɔːɹ ðiːz pɹəpˈoʊzəlz.
+DUMMY2/p281/p281_394.wav|36|wˈʌt ɑːɹ ðeɪ fɔːɹ ?
+DUMMY2/p287/p287_272.wav|77|ænd ðeɪ wɜː bˌiːɪŋ pˈeɪd ?
+DUMMY2/p288/p288_071.wav|47|hiː sˈiːmd tə lˈuːz hɪz fˈoʊkəs.
+DUMMY2/p335/p335_245.wav|49|ɐ fɹˈɛndʃɪp ðæt wɪl ɛndˈʊɹ.
+DUMMY2/p239/p239_061.wav|48|ˈɔːl mˈænɚɹ ʌv pɹɪkˈɔːʃən ænd pɹətˈɛkʃən ɑːɹ tˈeɪkən.
+DUMMY2/p254/p254_003.wav|41|sˈɪks spˈuːnz ʌv fɹˈɛʃ snˈoʊ pˈiːz, fˈaɪv θˈɪk slˈæbz ʌv blˈuː tʃˈiːz, ænd mˈeɪbiː ɐ snˈæk fɔːɹ hɜː bɹˈʌðɚ bˈɑːb.
+DUMMY2/p259/p259_282.wav|7|wˈɑːʃɪŋtən ɪz kənsˈuːmd baɪ ðə kɹˈaɪsɪs.
+DUMMY2/p253/p253_202.wav|70|sˈædli, ɪt kˈænt.
+DUMMY2/p318/p318_333.wav|19|bˌʌt wɛn wiː dˈuː ɪt ɪz ɡɹˈeɪt.
+DUMMY2/p351/p351_363.wav|33|juː wɪl nˈɛvɚ fɚɡˈɛt ðə klˈʌtʃɪŋ hˈɔːɹɚ.
+DUMMY2/p241/p241_374.wav|86|ðɛɹ ɪz nˈoʊ sˈɪɡnɪtʃɚ.
+DUMMY2/p272/p272_216.wav|69|ðə ɹɪpˈoːɹt ɪz dˈuː ˈaʊt nˈɛkst mˈʌnθ.
+DUMMY2/p330/p330_355.wav|1|aɪv ɡɑːt maɪ ˈoʊn aɪdˈiəz.
+DUMMY2/p270/p270_179.wav|8|ðɪ ˈaʊtkʌm ɪz nˈaʊ ɪn ˌaʊɚɹ ˈoʊn hˈændz.
+DUMMY2/p257/p257_079.wav|105|ɪt ɪz nˌɑːt lˈɑːŋ tˈɜːm, bˌʌt ˈaɪ nˈiːd tˈaɪm tə ɹɪkˈʌvɚ.
+DUMMY2/p257/p257_027.wav|105|ðeɪ ʃˌʊdəv ɐ mˈeɪdʒɚ ɹɪθˈɪŋk ɐbˌaʊt ðɪ ɪvˈɛnt fɔːɹ nˈɛkst jˈɪɹ.
+DUMMY2/p279/p279_118.wav|25|dˈʌz ðɪs mˈiːn.
+DUMMY2/p334/p334_058.wav|38|hˈoʊpfəli, ɪt wɪl biː bˈɪlt baɪ nˈɛkst jˈɪɹ.
+DUMMY2/p363/p363_178.wav|6|ðæt wʌzɐ hjˈuːdʒ ɛkspˈiəɹɪəns.
+DUMMY2/p376/p376_227.wav|71|"ænd θˈɔːt wiː wʊd ɡɛt ɐwˈeɪ wɪð ɪt."
+DUMMY2/p330/p330_411.wav|1|juː nˈoʊ, hiː wʌz stɹˈʌɡlɪŋ wɪð hɪz ɡˈeɪm ˈɔːl wˈiːk.
+DUMMY2/p326/p326_316.wav|28|ɪt sˈɜːtənli sˈaʊndᵻd ɪt æt tˈaɪmz.
+DUMMY2/p323/p323_048.wav|34|mˈæki wʌz æt hˈoʊm, ʌnˈeɪbəl tə wˈɑːtʃ.
+DUMMY2/p313/p313_422.wav|76|nˈaʊ, ðæt ɪz ɐ ɡˈʊd dˈiːl.
+DUMMY2/p364/p364_113.wav|88|ɪt wʌz dʒˈʌst ɡɹˈeɪt.
+DUMMY2/p286/p286_414.wav|63|ɪts dʒˈʌst ɐ tɹˈeɪnɪŋ θˈɪŋ.
+DUMMY2/p288/p288_229.wav|47|haʊˈɛvɚ, nˈoʊ fˈɜːðɚɹ ˈækʃən wʌz tˈeɪkən baɪ pəlˈiːs.
+DUMMY2/p259/p259_142.wav|7|wˌʌt hˈæpənd ɪn ðæt ɡˈeɪm ?
+DUMMY2/p297/p297_118.wav|42|ɪts tˈuː bˈɪɡ ɐ ɹˈɪsk tə tˈeɪk.
+DUMMY2/p313/p313_209.wav|76|ðə nˈaɪt ɪz jˈʌŋ.
+DUMMY2/p303/p303_279.wav|44|ˈaɪ bˈɔːt ɐ kˈɑːɹ æt ˈɔːkʃən.
+DUMMY2/p345/p345_166.wav|82|mˈɪlɚ wʌz ˈɛvɹi bˈɪt æz hˈæpi.
+DUMMY2/p333/p333_289.wav|64|ɪts ɡˌoʊɪŋ təbi kwˈaɪt ɐ tʃˈælɪndʒ.
+DUMMY2/p336/p336_323.wav|98|wˈʌn pˈeɪpɚ wʌz nˌɑːt ɹɪtˈɜːnd.
+DUMMY2/p271/p271_082.wav|27|hiː ɪz ɪnðə kjˈuː.
+DUMMY2/p314/p314_175.wav|51|ðɛɹ ɪz nˈoʊ sˈʌbstɪtˌuːt.
+DUMMY2/p248/p248_124.wav|99|ˈaɪ kˈænt ˈiːvən ɡɛt ˌɪntʊ ðɪ ɐ tˈiːm.
+DUMMY2/p297/p297_160.wav|42|tˈæks ɪz ɐ mˈæɾɚ fɔːɹ nˈæʃənəl ɡˈʌvɚnmənts.
+DUMMY2/p236/p236_299.wav|75|hˌaʊ dˈuː juː ɡɛt ɪt bˈæk ?
+DUMMY2/p248/p248_300.wav|99|ɪt wˈʌznt dʒˈʌst ðə kˈæɹɪktɚ ænd ˈɛnɚdʒi ʌvðə plˈeɪɪŋ.
+DUMMY2/p231/p231_429.wav|50|hiː ɪz ɑːnðə ɹˈɔŋ sˈaɪd.
+DUMMY2/p250/p250_368.wav|24|wiː pˌʊt ˌaʊɚ bˈɪd ɪn lˈæst nˈaɪt.
+DUMMY2/p376/p376_191.wav|71|"ˈaɪ æm tˈoʊɾəli sɚpɹˈaɪzd."
+DUMMY2/p250/p250_419.wav|24|ʃiː stˈɑːɹɾᵻd tə pˌʊt ˌɑːn wˈeɪt.
+DUMMY2/p239/p239_037.wav|48|hiː wˈɜːks æt ðɪ ˈɛɹpoːɹt.
+DUMMY2/p340/p340_165.wav|74|hiː wʌz vˈɛɹi fˈɪt.
+DUMMY2/p339/p339_258.wav|18|ðɛɹˌɑːɹ nˌɑːt tˈuː mɛni lˈaɪk hˌɪm.
+DUMMY2/p326/p326_266.wav|28|ɪt mˈeɪ ˈɑːlsoʊ biː hɜː lˈæst.
+DUMMY2/p231/p231_472.wav|50|hiː fˈɛlt ɪt wʌzðə ɹˈaɪt tˈaɪm.
+DUMMY2/p261/p261_411.wav|100|ˈaɪ hɐd ɐ fˈɔːɹtʃənət wˈɔːɹ.
+DUMMY2/p272/p272_359.wav|69|nˈaʊ, ðˈoʊ, hiː hɐz ɐn ɪnsˈɛntɪv.
+DUMMY2/p340/p340_015.wav|74|ðə ɡɹˈiːks jˈuːzd tʊ ɪmˈædʒɪn ðˌɐɾɪt wʌzɐ sˈaɪn fɹʌmðə ɡˈɑːdz tə foːɹtˈɛl wˈɔːɹ ɔːɹ hˈɛvi ɹˈeɪn.
+DUMMY2/p283/p283_022.wav|95|ðɪ ˈæktʃuːəl pɹˈaɪmɚɹi ɹˈeɪnboʊ ɑːbzˈɜːvd ɪz sˈɛd təbi ðɪ ɪfˈɛkt ʌv sˈuːpɚɹɪmpəzˈɪʃən əvə nˈʌmbɚɹ ʌv bˈoʊz.
+DUMMY2/p281/p281_334.wav|36|haʊˈɛvɚ, ðə ɡɹˈuːps dɪnˈaɪd ðə klˈeɪmz.
+DUMMY2/p318/p318_223.wav|19|wiː ɹɪmˈeɪn kəmˈɪɾᵻd tʊ ɪt, æz dˈʌz ðə ɡˈʌvɚnmənt.
+DUMMY2/p281/p281_039.wav|36|ðɪs fˈɪlm wɪl biː tˈoʊɾəli ˈɔːsʌm.
+DUMMY2/p270/p270_013.wav|8|sˌʌm hæv ɐksˈɛptᵻd ɪt æz ɐ mˈɪɹəkəl wɪðˌaʊt fˈɪzɪkəl ɛksplɐnˈeɪʃən.
+DUMMY2/p243/p243_047.wav|53|haʊˈɛvɚ, ðɛɹ ɪz ɐn ˈɪʃuː, ˌɪzənt ðˈɛɹ ?
+DUMMY2/p374/p374_122.wav|11|ðə kˈoːɹs ɪz ɪn ɡɹˈeɪt kəndˈɪʃən.
+DUMMY2/p302/p302_040.wav|30|ˌɑːn fjˈuːəl, ðə tʃˈænsɛlɚ hɐz ɐ nˈʌmbɚɹ ʌv ˈɑːpʃənz.
+DUMMY2/p254/p254_231.wav|41|ænd θˈɔːt wiː wʊd ɡɛt ɐwˈeɪ wɪð ɪt.
+DUMMY2/p246/p246_222.wav|5|ɪts nˌɑːt bɪfˌoːɹ tˈaɪm.
+DUMMY2/p262/p262_044.wav|45|ɪt ɪz dˈɪfɪkəlt fɔːɹ ˈɑːli.
+DUMMY2/p270/p270_005.wav|8|ʃiː kæn skˈuːp ðiːz θˈɪŋz ˌɪntʊ θɹˈiː ɹˈɛd bˈæɡz, ænd wiː wɪl ɡˌoʊ mˈiːt hɜː wˈɛnzdeɪ æt ðə tɹˈeɪn stˈeɪʃən.
+DUMMY2/p274/p274_340.wav|32|ðɪs ɪz ɐ hɪstˈɔːɹɪk əkˈeɪʒən.
+DUMMY2/p329/p329_045.wav|103|ˈaɪ hˈoʊp juː wɪl lˈiːv ɪt æt ðˈæt.
+DUMMY2/p285/p285_188.wav|2|ˌɛni tʃˈeɪndʒ wʊd biː sˈʌbdʒɛkt tə ðə skˈɑːɾɪʃ pˈɑːɹləmənts ɐpɹˈuːvəl.
+DUMMY2/p260/p260_193.wav|81|ðə ʃˈædoʊ tʃˈænsɛlɚɹ ɪz ɐwˈeɪ ˌɑːn hˈɑːlɪdˌeɪ.
+DUMMY2/p259/p259_371.wav|7|hiː wʌz ʌnˈeɪbəl tə kˈʌm.
+DUMMY2/p275/p275_052.wav|40|sˈɛvɹəl ˈʌðɚ pjˈuːpəlz ænd stˈæf wɜː sˈiəɹɪəsli ˈɪndʒɚd ɪnðɪ ˈæksɪdənt.
+DUMMY2/p233/p233_159.wav|84|bˌʌt hiː stɹˈɛst ðætðə pˈɑːɹtnɚʃˌɪp ɪz nˌɑːɾə kənstɹˈʌkʃən kˈʌmpəni.
+DUMMY2/p277/p277_312.wav|89|ɪt wɪl wˈɜːk.
+DUMMY2/p295/p295_211.wav|92|lˈiːvɪŋ ðə lˈeɪbɚ pˈɑːɹɾi ɪz wˈʌn θˈɪŋ.
+DUMMY2/p297/p297_150.wav|42|ɪt ɪz ðə wˈɛlθɪəst ɪn jˈʊɹəp.
+DUMMY2/p305/p305_026.wav|54|hiː ˈædᵻd, haʊˈɛvɚ, ðæt ˈɔːl ˈɑːpʃənz ɑːɹ ˌʌndɚ ɹɪvjˈuː.
+DUMMY2/p292/p292_121.wav|13|ðɪs wʊd nˌɑːt biː maɪ fˈɜːst tʃˈɔɪs.
+DUMMY2/p253/p253_346.wav|70|ɪt ɪz ðə hˈɑːlɪdˌeɪ pɹˈoʊɡɹæm wɪð ɐ mˈɔːɹɡɪdʒ.
+DUMMY2/p363/p363_171.wav|6|hiː dˈɪdnt nˈoʊ wˌɛɹ tə lˈʊk.
+DUMMY2/p233/p233_128.wav|84|ɪt ɪz stˈɪl tˈuː ˈɜːli fɔːɹ ˌɛni lˈaɪkli kəntˈɛndɚz tə hæv ɪmˈɜːdʒd.
+DUMMY2/p251/p251_137.wav|9|wiː ɑːɹ kˈɜːɹəntli kənsˈʌltɪŋ wɪð ɐ wˈaɪd ɹˈeɪndʒ ʌv ˈɪntɹəstᵻd pˈɑːɹɾɪz.
+DUMMY2/p334/p334_034.wav|38|ɐpˈɔɪntᵻd dʒˈɛnɚɹəl sˈɛkɹətɹi lˈæst sɛptˈɛmbɚ.
+DUMMY2/p286/p286_225.wav|63|ðɪs wɪl tˈeɪk sˈɛvɹəl wˈiːks.
+DUMMY2/p363/p363_183.wav|6|pˈʌblɪk sˈeɪfti ɪz pˈæɹəmˌaʊnt.
+DUMMY2/p256/p256_207.wav|90|ˈæftɚ ðæt tˈaɪm, ðə mˈɑːɹkɪt ɪtsˈɛlf wɪl sˈɛt ðə pɹˈaɪsᵻz.
+DUMMY2/p273/p273_311.wav|56|dʒˈɑːb lˈɔsᵻz wɜːɹ ˈɑːlsoʊ ɐnˈaʊnst.
+DUMMY2/p274/p274_425.wav|32|ðə pɹədʒˈɛkʃənz ɑːɹ vˈɛɹi pˈɑːzɪtˌɪv fɔːɹ sˈaʊθ ˈæfɹɪkə.
+DUMMY2/p254/p254_065.wav|41|ðæts ðə dˈeɪ dʒˈɑːb.
+DUMMY2/p335/p335_123.wav|49|wˈæɡnɚ wʌz nˈɛvɚ lˈaɪk ðˈɪs.
+DUMMY2/p258/p258_105.wav|26|wiː duːnˌɑːt ɛkspˈɛkt ˌɛni sˈɜːplʌs.
+DUMMY2/p286/p286_294.wav|63|ɪt wʌz ɐn ˈiːzi dᵻsˈɪʒən tə kˈʌm hˈɪɹ.
+DUMMY2/p361/p361_218.wav|79|bˌʌt wiː wɜː ɹˈɔŋ.
+DUMMY2/p247/p247_426.wav|14|bˌiːɪŋ kˈæptɪn ʌv ðɪs klˈʌb ɪz fæntˈæstɪk.
+DUMMY2/p266/p266_391.wav|20|ɪn tˈaɪm, mˈeɪ pɹˈuːv ɐ wˈɜːði səksˈɛsɚ tə bˈɪli dˈɑːdz.
+DUMMY2/p253/p253_116.wav|70|ɪt ɪz sˌoʊ sˈæd.
+DUMMY2/p261/p261_081.wav|100|ˌaʊɚ mˈʌðɚɹ ɪz vˈɛɹi wˈʌɹɪd.
+DUMMY2/p268/p268_131.wav|87|bˌʌt ðˈɛn ðeɪ skˈoːɹd ðɛɹ fˈoːɹθ.
+DUMMY2/p229/p229_192.wav|67|ˈaɪ hæv ðə fˈɜːst sˈɪks mˈʌnθs ʌv nˈɛkst sˈiːzən tə pɹˈuːv maɪsˈɛlf.
+DUMMY2/p275/p275_260.wav|40|ðeɪ wˈɑːnt tə ʃˈʌt ðə skˈɑːɾɪʃ ˈɑːfɪs.
+DUMMY2/p313/p313_109.wav|76|nˈʌθɪŋ ɪz bˌiːɪŋ ˈɑːfɚd ɪn ɛkstʃˈeɪndʒ.
+DUMMY2/p347/p347_072.wav|46|θˈæŋkfəli, mˈɪstɚ kˈæmbəl wʌz ˈeɪbəl tə hˈɛlp.
+DUMMY2/p298/p298_334.wav|68|hˈoʊpfəli, ðə hˈoʊl ʌv skˈɑːɾɪʃ ɹˈʌɡbi wʌz pˈeɪɪŋ ɐtˈɛnʃən.
+DUMMY2/p271/p271_232.wav|27|dʒˈɪm wˈɑːlᵻs, ðə dʒˈʌstɪs mˈɪnɪstɚ, ɐknˈɑːlɪdʒd ðæt pɹˈɪzənɚ nˈʌmbɚz wɜːɹ ɐ kənsˈɜːn.
+DUMMY2/p283/p283_056.wav|95|fɚðə mˈiːntaɪm, ðˈoʊ, ðə sˈaɪnz ɑːɹ ɡˈʊd.
+DUMMY2/p255/p255_239.wav|31|ɪts ðə sˈeɪm æz ɡlˈæzɡoʊ.
+DUMMY2/p267/p267_244.wav|0|wiː hæv kˈʌm ɐ lˈɑːŋ wˈeɪ ɪnðə lˈæst fjˈuː sˈɛʃənz.
+DUMMY2/p340/p340_403.wav|74|ˈaɪ hɐd ɐ bˈɔːl tədˈeɪ.
+DUMMY2/p230/p230_083.wav|35|ɪt mˌaɪt tʃˈeɪndʒ jʊɹ lˈaɪf.
+DUMMY2/p299/p299_403.wav|58|wiː wɪl hæv tə sˈiː, bˌʌt ɪt mˌeɪks juː θˈɪŋk.
+DUMMY2/p343/p343_128.wav|21|ðˈɛn kˈeɪm ðə kɹˈʌntʃ.
+DUMMY2/p297/p297_021.wav|42|ðə dˈɪfɹəns ɪnðə ɹˈeɪnboʊ dɪpˈɛndz kənsˈɪdɚɹəbli əpˌɑːn ðə sˈaɪz ʌvðə dɹˈɑːps, ænd ðə wˈɪdθ ʌvðə kˈʌlɚd bˈænd ˈɪnkɹiːsᵻz æz ðə sˈaɪz ʌvðə dɹˈɑːps ˈɪnkɹiːsᵻz.
+DUMMY2/p298/p298_275.wav|68|ðɛɹˌɑːɹ lˈɑːts ʌv ðiːz wˈɪmɪn ɪn fˈɪnlənd.
+DUMMY2/p347/p347_286.wav|46|ɪt ɪz dʒˈʌst tˈuː lˈɑːŋ sˈɪns ðə wˈɔːɹ.
+DUMMY2/p239/p239_445.wav|48|ˈiːðɚ ɡɹˈuːp ɪz lˈɪvɪŋ ɪn fˈæntəsi lˈænd.
+DUMMY2/p286/p286_003.wav|63|sˈɪks spˈuːnz ʌv fɹˈɛʃ snˈoʊ pˈiːz, fˈaɪv θˈɪk slˈæbz ʌv blˈuː tʃˈiːz, ænd mˈeɪbiː ɐ snˈæk fɔːɹ hɜː bɹˈʌðɚ bˈɑːb.
+DUMMY2/p299/p299_082.wav|58|ˈaɪ stˈɪl fˈiːl lˈaɪk ɐ wˈiː bˈɔɪ.
+DUMMY2/p306/p306_213.wav|12|ɪt ɪz lˈaɪk bˌiːɪŋ ɐ kwˈɑːlɪfˌaɪɚɹ ɐɡˈɛn.
+DUMMY2/p339/p339_305.wav|18|wiː nˈoʊ ðə ɡˈoʊlz wɪl kˈʌm.
+DUMMY2/p265/p265_274.wav|73|ðɪs tˈaɪm, fɔːɹ ɹˈeɪndʒɚz, ɪt ɪz sˈɜːtənli ðə lˈæɾɚ.
+DUMMY2/p310/p310_382.wav|17|ɪt hɐz ðə bˈæŋk ʌv skˈɑːtlənd bɪhˈaɪnd ɪt.
+DUMMY2/p335/p335_403.wav|49|ˈɛnɪwˌeɪ, ˈiːvən ɪf ðeɪ dˈɪdnt ɪt wˌʊdəntəv mˈæɾɚd.
+DUMMY2/p246/p246_330.wav|5|ˈaɪ wʊd biː kwˈaɪt hˈæpi fɚðə mˈʌni təbi ɡˈɪvən bˈæk.
+DUMMY2/p288/p288_386.wav|47|ðɛɹ ɪz ɐ səlˈuːʃən, ʃiː bɪlˈiːvz.
+DUMMY2/p234/p234_019.wav|3|sˈɪns ðˈɛn fˈɪzɪsˌɪsts hæv fˈaʊnd ðˌɐɾɪt ɪz nˌɑːt ɹɪflˈɛkʃən, bˌʌt ɹɪfɹˈækʃən baɪ ðə ɹˈeɪndɹɑːps wˌɪtʃ kˈɔːzᵻz ðə ɹˈeɪnboʊz.
+DUMMY2/p287/p287_408.wav|77|fˈɜːst, wiː hɐd ðə bˈæɾəl ʌv bɹˈɪtən.
+DUMMY2/p286/p286_249.wav|63|ʃiː wɪl ɐtˈɛnd ɪn dʒuːlˈaɪ.
+DUMMY2/p251/p251_235.wav|9|aɪd nˈɛvɚ sˈiːn ɐ plˈeɪ ɐbˈaʊt mˌiː.
+DUMMY2/p347/p347_291.wav|46|ɪnʃˈʊɹəns wɪl biː kˈʌvɚd baɪ ðə ɹɪsˈiːvɪŋ ɡˈælɚɹiz.
+DUMMY2/p257/p257_058.wav|105|ɪt ɪz nˌɑːt ɡɹˈeɪt ˈɑːɹt.
+DUMMY2/p231/p231_471.wav|50|dˈɛnᵻs wʌz nˌɑːt sˌoʊ ʃˈʊɹ.
+DUMMY2/p341/p341_107.wav|66|ðɛɹwˌʌz ɡɹˈeɪt səpˈoːɹt ˈɔːl ɹˈaʊnd ðə ɹˈaʊt.
+DUMMY2/p264/p264_160.wav|65|ɪt wʌz klˈɪɹli nˌɑːɾə bˈæɾəl.
+DUMMY2/p252/p252_155.wav|55|ˈaɪ θˈɪŋk, ðˈɛɹfoːɹ ˈaɪ æm ?
+DUMMY2/p336/p336_264.wav|98|fˈɜːɡəsən mˈʌst tˈeɪk ðə blˈeɪm.
+DUMMY2/p274/p274_142.wav|32|ðə ɹˌɛfɚɹˈiː fˈeɪsᵻz ɐ mˈæsɪv dʒˈɑːb.
+DUMMY2/p303/p303_005.wav|44|ʃiː kæn skˈuːp ðiːz θˈɪŋz ˌɪntʊ θɹˈiː ɹˈɛd bˈæɡz, ænd wiː wɪl ɡˌoʊ mˈiːt hɜː wˈɛnzdeɪ æt ðə tɹˈeɪn stˈeɪʃən.
+DUMMY2/p233/p233_240.wav|84|ðə sˈɪŋɚɹ ɪz ɛkspˈɛktᵻd təbi ɪn hˈɑːspɪɾəl fɔːɹ sˈɛvɹəl dˈeɪz.
+DUMMY2/p333/p333_220.wav|64|ðɪs pɹˈɑːsɛs ʌv ɐtɹˈɪʃən ɪz ɛkspˈɛktᵻd tə kəntˈɪnjuː.
+DUMMY2/p285/p285_303.wav|2|ˈælɪks smˈɪθ hɐzbɪn ɐ mˈæsɪv ˈɪnfluːəns ˌɑːn maɪ kɚɹˈɪɹ æz wˈɛl.
+DUMMY2/p277/p277_348.wav|89|tə dˈuː sˌoʊ hiː ɹˈɛkənz ðˌæɾə ɡˈʊd ˈoʊpənɪŋ ɹɪzˈʌlt ɪz ɪsˈɛnʃəl.
+DUMMY2/p311/p311_290.wav|4|ˈaɪ æm nˌɑːt ɪn dɪnˈaɪəl.
+DUMMY2/p286/p286_316.wav|63|ˈaɪ æm ɐ ɹˈiːteɪlɚ baɪ nˈeɪtʃɚ.
+DUMMY2/p306/p306_119.wav|12|kəmplˈiːʃən ɪz ɛkspˈɛktᵻd baɪ ɑːktˈoʊbɚ ðə fˈɑːloʊɪŋ jˈɪɹ.
+DUMMY2/p240/p240_028.wav|93|ɪz ðɪs ˈækjʊɹət?
+DUMMY2/p238/p238_295.wav|37|ɪt hɐzbɪn ɹɪkˈoːɹdᵻd twˈaɪs.
+DUMMY2/p278/p278_049.wav|10|hiː wɪl nˈiːd ðæt məʃˈiːn.
+DUMMY2/p351/p351_282.wav|33|wiː dʒˈʌst wˈɪʃ ðeɪ hɐd dˈʌn sˌoʊ bɪfˈoːɹ.
+DUMMY2/p267/p267_348.wav|0|mˈɛnɪəv ðiːz pɹˈɑːpɚɾɪz ɑːɹ loʊkˈeɪɾᵻd ɪnðə sˈaʊθ ʌv ˈɪŋɡlənd.
+DUMMY2/p312/p312_360.wav|62|ænd skˈɑːtlənd ɪz nˈoʊ dˈɪfɹənt.
+DUMMY2/p311/p311_324.wav|4|hiː pɹɪtˈɛndᵻd nˌɑːt tə kˈɛɹ.
+DUMMY2/p283/p283_389.wav|95|skɹˈuːtɪni baɪ ðə jˌʊɹəpˈiən pˈɑːɹləmənt ɪz lˈɪmɪɾᵻd.
+DUMMY2/p266/p266_079.wav|20|hiː ɪz ɪnðə kjˈuː.
+DUMMY2/p274/p274_424.wav|32|nˌɑːt ðæt skˈɑːtlənd kæn klˈeɪm ðə mˈɔːɹəl hˈaɪ ɡɹˈaʊnd.
+DUMMY2/p303/p303_169.wav|44|fɔːɹ ˈæθliːts ɪn ˌaʊɚ kˈɜːɹənt klˈaɪmət, ðɛɹ spˈoːɹt ɪz ðɛɹ lˈaɪvlihˌʊd.
+DUMMY2/p252/p252_237.wav|55|juː nˈoʊ ðə tˈaɪp.
+DUMMY2/p323/p323_115.wav|34|pˈɑːɹts ʌvðə sˈɪstəm ɑːɹ ɔːlɹˌɛdi ˌoʊvɚstɹˈɛtʃt.
+DUMMY2/p361/p361_013.wav|79|sˌʌm hæv ɐksˈɛptᵻd ɪt æz ɐ mˈɪɹəkəl wɪðˌaʊt fˈɪzɪkəl ɛksplɐnˈeɪʃən.
+DUMMY2/p333/p333_356.wav|64|wˌɪtʃ hiː kæn dˈuː.
+DUMMY2/p241/p241_029.wav|86|haʊˈɛvɚ, ðə fˈɑːloʊɪŋ jˈɪɹ ðə kˈænsɚ ɹɪtˈɜːnd.
+DUMMY2/p248/p248_371.wav|99|wˈɛðɚ hɪz stˈæns ɪz ʃˈɛɹd baɪ ðɪ ˈɪnkʌmˌɪŋ mˈænɪdʒɚɹ ɪz ɐnˈʌðɚ mˈæɾɚ.
+DUMMY2/p260/p260_007.wav|81|ðə ɹˈeɪnboʊ ɪz ɐ dɪvˈɪʒən ʌv wˈaɪt lˈaɪt ˌɪntʊ mˈɛni bjˈuːɾɪfəl kˈʌlɚz.
+DUMMY2/p287/p287_257.wav|77|ðə kənsˈɜːnz ɑːɹ ðə sˈeɪm.
+DUMMY2/p263/p263_125.wav|39|ɪt ˌɪzənt ɐ hˈæpi mˈɛmɚɹi.
+DUMMY2/p277/p277_258.wav|89|ɪmˈiːdɪət ˈækʃən mˈʌst biː tˈeɪkən.
+DUMMY2/p363/p363_219.wav|6|ɪt wʌz ɪmpˈoːɹtənt ɪn tɹˈeɪnɪŋ tˈɜːmz.
+DUMMY2/p269/p269_191.wav|94|maɪ mˈeɪn kənsˈɜːn ɪz ðæt pˈʌblɪk hˈɛlθ ɪz nˌɑːt pˌʊt æt ɹˈɪsk.
+DUMMY2/p262/p262_020.wav|45|mˈɛni kˈɑːmplᵻkˌeɪɾᵻd aɪdˈiəz ɐbˌaʊt ðə ɹˈeɪnboʊ hɐvbɪn fˈɔːɹmd.
+DUMMY2/p273/p273_023.wav|56|ɪf ðə ɹˈɛd ʌvðə sˈɛkənd bˈoʊ fˈɔːlz əpˌɑːn ðə ɡɹˈiːn ʌvðə fˈɜːst, ðə ɹɪzˈʌlt ɪz tə ɡˈɪv ɐ bˈoʊ wɪð ɐn ɐbnˈoːɹməli wˈaɪd jˈɛloʊ bˈænd, sˈɪns ɹˈɛd ænd ɡɹˈiːn lˈaɪt wɛn mˈɪkst fˈɔːɹm jˈɛloʊ.
+DUMMY2/p278/p278_029.wav|10|ðeɪ hæv nˈaʊ bˌɪn bˈænd fɹʌm kˈɛltɪk pˈɑːɹk fɔːɹ lˈaɪf.
+DUMMY2/p310/p310_065.wav|17|ˈaɪ hæv hɐd nˈoʊ sˈoʊʃəl lˈaɪf æt ˈɔːl.
+DUMMY2/p255/p255_352.wav|31|hiːz vˈɛɹi ɛksplˈoʊsɪv.
+DUMMY2/p376/p376_019.wav|71|"sˈɪns ðˈɛn fˈɪzɪsˌɪsts hæv fˈaʊnd ðˌɐɾɪt ɪz nˌɑːt ɹɪflˈɛkʃən, bˌʌt ɹɪfɹˈækʃən baɪ ðə ɹˈeɪndɹɑːps wˌɪtʃ kˈɔːzᵻz ðə ɹˈeɪnboʊz. "
+DUMMY2/p263/p263_307.wav|39|ɪz ðɛɹ ɐ wˈeɪɾɪŋ lˈɪst ?
+DUMMY2/p249/p249_258.wav|80|ðeɪ mˈʌst plˈeɪ fɔːɹ ˈiːtʃ ˈʌðɚ.
+DUMMY2/p258/p258_111.wav|26|mˈeɪbiː ðɪs bˈæɾəl hɐzbɪn.
+DUMMY2/p316/p316_129.wav|85|ðɛɹ kæn biː nˈoʊ kˈɑːmpɹəmˌaɪz ˌɑːn ðæt dɪmˈænd.

vits/filelists/vctk_audio_sid_text_val_filelist.txt

@@ -0,0 +1,100 @@
+DUMMY2/p364/p364_240.wav|88|It had happened to him.
+DUMMY2/p280/p280_148.wav|52|It is open season on the Old Firm.
+DUMMY2/p231/p231_320.wav|50|However, he is a coach, and he remains a coach at heart.
+DUMMY2/p282/p282_129.wav|83|It is not a U-turn.
+DUMMY2/p254/p254_015.wav|41|The Greeks used to imagine that it was a sign from the gods to foretell war or heavy rain.
+DUMMY2/p228/p228_285.wav|57|The songs are just so good.
+DUMMY2/p334/p334_307.wav|38|If they don't, they can expect their funding to be cut.
+DUMMY2/p287/p287_081.wav|77|I've never seen anything like it.
+DUMMY2/p247/p247_083.wav|14|It is a job creation scheme.)
+DUMMY2/p264/p264_051.wav|65|We were leading by two goals.)
+DUMMY2/p335/p335_058.wav|49|Let's see that increase over the years.
+DUMMY2/p236/p236_225.wav|75|There is no quick fix.
+DUMMY2/p374/p374_353.wav|11|And that brings us to the point.
+DUMMY2/p272/p272_076.wav|69|Sounds like The Sixth Sense?
+DUMMY2/p271/p271_152.wav|27|The petition was formally presented at Downing Street yesterday.
+DUMMY2/p228/p228_127.wav|57|They've got to account for it.
+DUMMY2/p276/p276_223.wav|106|It's been a humbling year.
+DUMMY2/p262/p262_248.wav|45|The project has already secured the support of Sir Sean Connery.
+DUMMY2/p314/p314_086.wav|51|The team this year is going places.
+DUMMY2/p225/p225_038.wav|101|Diving is no part of football.
+DUMMY2/p279/p279_088.wav|25|The shareholders will vote to wind up the company on Friday morning.
+DUMMY2/p272/p272_018.wav|69|Aristotle thought that the rainbow was caused by reflection of the sun's rays by the rain.
+DUMMY2/p256/p256_098.wav|90|She told The Herald.
+DUMMY2/p261/p261_218.wav|100|All will be revealed in due course.
+DUMMY2/p265/p265_063.wav|73|IT shouldn't come as a surprise, but it does.
+DUMMY2/p314/p314_042.wav|51|It is all about people being assaulted, abused.
+DUMMY2/p241/p241_188.wav|86|I wish I could say something.
+DUMMY2/p283/p283_111.wav|95|It's good to have a voice.
+DUMMY2/p275/p275_006.wav|40|When the sunlight strikes raindrops in the air, they act as a prism and form a rainbow.
+DUMMY2/p228/p228_092.wav|57|Today I couldn't run on it.
+DUMMY2/p295/p295_343.wav|92|The atmosphere is businesslike.
+DUMMY2/p228/p228_187.wav|57|They will run a mile.
+DUMMY2/p294/p294_317.wav|104|It didn't put me off.
+DUMMY2/p231/p231_445.wav|50|It sounded like a bomb.
+DUMMY2/p272/p272_086.wav|69|Today she has been released.
+DUMMY2/p255/p255_210.wav|31|It was worth a photograph.
+DUMMY2/p229/p229_060.wav|67|And a film maker was born.
+DUMMY2/p260/p260_232.wav|81|The Home Office would not release any further details about the group.
+DUMMY2/p245/p245_025.wav|59|Johnson was pretty low.
+DUMMY2/p333/p333_185.wav|64|This area is perfect for children.
+DUMMY2/p244/p244_242.wav|78|He is a man of the people.
+DUMMY2/p376/p376_187.wav|71|"It is a terrible loss."
+DUMMY2/p239/p239_156.wav|48|It is a good lifestyle.
+DUMMY2/p307/p307_037.wav|22|He released a half-dozen solo albums.
+DUMMY2/p305/p305_185.wav|54|I am not even thinking about that.
+DUMMY2/p272/p272_081.wav|69|It was magic.
+DUMMY2/p302/p302_297.wav|30|I'm trying to stay open on that.
+DUMMY2/p275/p275_320.wav|40|We are in the end game.
+DUMMY2/p239/p239_231.wav|48|Then we will face the Danish champions.
+DUMMY2/p268/p268_301.wav|87|It was only later that the condition was diagnosed.
+DUMMY2/p336/p336_088.wav|98|They failed to reach agreement yesterday.
+DUMMY2/p278/p278_255.wav|10|They made such decisions in London.
+DUMMY2/p361/p361_132.wav|79|That got me out.
+DUMMY2/p307/p307_146.wav|22|You hope he prevails.
+DUMMY2/p244/p244_147.wav|78|They could not ignore the will of parliament, he claimed.
+DUMMY2/p294/p294_283.wav|104|This is our unfinished business.
+DUMMY2/p283/p283_300.wav|95|I would have the hammer in the crowd.
+DUMMY2/p239/p239_079.wav|48|I can understand the frustrations of our fans.
+DUMMY2/p264/p264_009.wav|65|There is , according to legend, a boiling pot of gold at one end. )
+DUMMY2/p307/p307_348.wav|22|He did not oppose the divorce.
+DUMMY2/p304/p304_308.wav|72|We are the gateway to justice.
+DUMMY2/p281/p281_056.wav|36|None has ever been found.
+DUMMY2/p267/p267_158.wav|0|We were given a warm and friendly reception.
+DUMMY2/p300/p300_169.wav|102|Who do these people think they are?
+DUMMY2/p276/p276_177.wav|106|They exist in name alone.
+DUMMY2/p228/p228_245.wav|57|It is a policy which has the full support of the minister.
+DUMMY2/p300/p300_303.wav|102|I'm wondering what you feel about the youngest.
+DUMMY2/p362/p362_247.wav|15|This would give Scotland around eight members.
+DUMMY2/p326/p326_031.wav|28|United were in control without always being dominant.
+DUMMY2/p361/p361_288.wav|79|I did not think it was very proper.
+DUMMY2/p286/p286_145.wav|63|Tiger is not the norm.
+DUMMY2/p234/p234_071.wav|3|She did that for the rest of her life.
+DUMMY2/p263/p263_296.wav|39|The decision was announced at its annual conference in Dunfermline.
+DUMMY2/p323/p323_228.wav|34|She became a heroine of my childhood.
+DUMMY2/p280/p280_346.wav|52|It was a bit like having children.
+DUMMY2/p333/p333_080.wav|64|But the tragedy did not stop there.
+DUMMY2/p226/p226_268.wav|43|That decision is for the British Parliament and people.
+DUMMY2/p362/p362_314.wav|15|Is that right?
+DUMMY2/p240/p240_047.wav|93|It is so sad.
+DUMMY2/p250/p250_207.wav|24|You could feel the heat.
+DUMMY2/p273/p273_176.wav|56|Neither side would reveal the details of the offer.
+DUMMY2/p316/p316_147.wav|85|And frankly, it's been a while.
+DUMMY2/p265/p265_047.wav|73|It is unique.
+DUMMY2/p336/p336_353.wav|98|Sometimes you get them, sometimes you don't.
+DUMMY2/p230/p230_376.wav|35|This hasn't happened in a vacuum.
+DUMMY2/p308/p308_209.wav|107|There is great potential on this river.
+DUMMY2/p250/p250_442.wav|24|We have not yet received a letter from the Irish.
+DUMMY2/p260/p260_037.wav|81|It's a fact.
+DUMMY2/p299/p299_345.wav|58|We're very excited and challenged by the project.
+DUMMY2/p269/p269_218.wav|94|A Grampian Police spokesman said.
+DUMMY2/p306/p306_014.wav|12|To the Hebrews it was a token that there would be no more universal floods.
+DUMMY2/p271/p271_292.wav|27|It's a record label, not a form of music.
+DUMMY2/p247/p247_225.wav|14|I am considered a teenager.)
+DUMMY2/p294/p294_094.wav|104|It should be a condition of employment.
+DUMMY2/p269/p269_031.wav|94|Is this accurate?
+DUMMY2/p275/p275_116.wav|40|It's not fair.
+DUMMY2/p265/p265_006.wav|73|When the sunlight strikes raindrops in the air, they act as a prism and form a rainbow.
+DUMMY2/p285/p285_072.wav|2|Mr Irvine said Mr Rafferty was now in good spirits.
+DUMMY2/p270/p270_167.wav|8|We did what we had to do.
+DUMMY2/p360/p360_397.wav|60|It is a relief.

vits/filelists/vctk_audio_sid_text_val_filelist.txt.cleaned

@@ -0,0 +1,100 @@
+DUMMY2/p364/p364_240.wav|88|ɪt hɐd hˈæpənd tə hˌɪm.
+DUMMY2/p280/p280_148.wav|52|ɪt ɪz ˈoʊpən sˈiːzən ɑːnðɪ ˈoʊld fˈɜːm.
+DUMMY2/p231/p231_320.wav|50|haʊˈɛvɚ, hiː ɪz ɐ kˈoʊtʃ, ænd hiː ɹɪmˈeɪnz ɐ kˈoʊtʃ æt hˈɑːɹt.
+DUMMY2/p282/p282_129.wav|83|ɪt ɪz nˌɑːɾə jˈuːtˈɜːn.
+DUMMY2/p254/p254_015.wav|41|ðə ɡɹˈiːks jˈuːzd tʊ ɪmˈædʒɪn ðˌɐɾɪt wʌzɐ sˈaɪn fɹʌmðə ɡˈɑːdz tə foːɹtˈɛl wˈɔːɹ ɔːɹ hˈɛvi ɹˈeɪn.
+DUMMY2/p228/p228_285.wav|57|ðə sˈɔŋz ɑːɹ dʒˈʌst sˌoʊ ɡˈʊd.
+DUMMY2/p334/p334_307.wav|38|ɪf ðeɪ dˈoʊnt, ðeɪ kæn ɛkspˈɛkt ðɛɹ fˈʌndɪŋ təbi kˈʌt.
+DUMMY2/p287/p287_081.wav|77|aɪv nˈɛvɚ sˈiːn ˈɛnɪθˌɪŋ lˈaɪk ɪt.
+DUMMY2/p247/p247_083.wav|14|ɪt ɪz ɐ dʒˈɑːb kɹiːˈeɪʃən skˈiːm.
+DUMMY2/p264/p264_051.wav|65|wiː wɜː lˈiːdɪŋ baɪ tˈuː ɡˈoʊlz.
+DUMMY2/p335/p335_058.wav|49|lˈɛts sˈiː ðæt ˈɪnkɹiːs ˌoʊvɚ ðə jˈɪɹz.
+DUMMY2/p236/p236_225.wav|75|ðɛɹ ɪz nˈoʊ kwˈɪk fˈɪks.
+DUMMY2/p374/p374_353.wav|11|ænd ðæt bɹˈɪŋz ˌʌs tə ðə pˈɔɪnt.
+DUMMY2/p272/p272_076.wav|69|sˈaʊndz lˈaɪk ðə sˈɪksθ sˈɛns?
+DUMMY2/p271/p271_152.wav|27|ðə pətˈɪʃən wʌz fˈɔːɹməli pɹɪzˈɛntᵻd æt dˈaʊnɪŋ stɹˈiːt jˈɛstɚdˌeɪ.
+DUMMY2/p228/p228_127.wav|57|ðeɪv ɡɑːt tʊ ɐkˈaʊnt fɔːɹ ɪt.
+DUMMY2/p276/p276_223.wav|106|ɪts bˌɪn ɐ hˈʌmblɪŋ jˈɪɹ.
+DUMMY2/p262/p262_248.wav|45|ðə pɹˈɑːdʒɛkt hɐz ɔːlɹˌɛdi sɪkjˈʊɹd ðə səpˈoːɹt ʌv sˌɜː ʃˈɔːn kɑːnɚɹi.
+DUMMY2/p314/p314_086.wav|51|ðə tˈiːm ðɪs jˈɪɹ ɪz ɡˌoʊɪŋ plˈeɪsᵻz.
+DUMMY2/p225/p225_038.wav|101|dˈaɪvɪŋ ɪz nˈoʊ pˈɑːɹt ʌv fˈʊtbɔːl.
+DUMMY2/p279/p279_088.wav|25|ðə ʃˈɛɹhoʊldɚz wɪl vˈoʊt tə wˈaɪnd ˈʌp ðə kˈʌmpəni ˌɑːn fɹˈaɪdeɪ mˈɔːɹnɪŋ.
+DUMMY2/p272/p272_018.wav|69|ˈæɹɪstˌɑːɾəl θˈɔːt ðætðə ɹˈeɪnboʊ wʌz kˈɔːzd baɪ ɹɪflˈɛkʃən ʌvðə sˈʌnz ɹˈeɪz baɪ ðə ɹˈeɪn.
+DUMMY2/p256/p256_098.wav|90|ʃiː tˈoʊld ðə hˈɛɹəld.
+DUMMY2/p261/p261_218.wav|100|ˈɔːl wɪl biː ɹɪvˈiːld ɪn dˈuː kˈoːɹs.
+DUMMY2/p265/p265_063.wav|73|ɪt ʃˌʊdənt kˈʌm æz ɐ sɚpɹˈaɪz, bˌʌt ɪt dˈʌz.
+DUMMY2/p314/p314_042.wav|51|ɪt ɪz ˈɔːl ɐbˌaʊt pˈiːpəl bˌiːɪŋ ɐsˈɑːltᵻd, ɐbjˈuːsd.
+DUMMY2/p241/p241_188.wav|86|ˈaɪ wˈɪʃ ˈaɪ kʊd sˈeɪ sˈʌmθɪŋ.
+DUMMY2/p283/p283_111.wav|95|ɪts ɡˈʊd tə hæv ɐ vˈɔɪs.
+DUMMY2/p275/p275_006.wav|40|wˌɛn ðə sˈʌnlaɪt stɹˈaɪks ɹˈeɪndɹɑːps ɪnðɪ ˈɛɹ, ðeɪ ˈækt æz ɐ pɹˈɪzəm ænd fˈɔːɹm ɐ ɹˈeɪnboʊ.
+DUMMY2/p228/p228_092.wav|57|tədˈeɪ ˈaɪ kˌʊdənt ɹˈʌn ˈɑːn ɪt.
+DUMMY2/p295/p295_343.wav|92|ðɪ ˈætməsfˌɪɹ ɪz bˈɪznəslˌaɪk.
+DUMMY2/p228/p228_187.wav|57|ðeɪ wɪl ɹˈʌn ɐ mˈaɪl.
+DUMMY2/p294/p294_317.wav|104|ɪt dˈɪdnt pˌʊt mˌiː ˈɔf.
+DUMMY2/p231/p231_445.wav|50|ɪt sˈaʊndᵻd lˈaɪk ɐ bˈɑːm.
+DUMMY2/p272/p272_086.wav|69|tədˈeɪ ʃiː hɐzbɪn ɹɪlˈiːsd.
+DUMMY2/p255/p255_210.wav|31|ɪt wʌz wˈɜːθ ɐ fˈoʊɾəɡɹˌæf.
+DUMMY2/p229/p229_060.wav|67|ænd ɐ fˈɪlm mˈeɪkɚ wʌz bˈɔːɹn.
+DUMMY2/p260/p260_232.wav|81|ðə hˈoʊm ˈɑːfɪs wʊd nˌɑːt ɹɪlˈiːs ˌɛni fˈɜːðɚ diːtˈeɪlz ɐbˌaʊt ðə ɡɹˈuːp.
+DUMMY2/p245/p245_025.wav|59|dʒˈɑːnsən wʌz pɹˈɪɾi lˈoʊ.
+DUMMY2/p333/p333_185.wav|64|ðɪs ˈɛɹiə ɪz pˈɜːfɛkt fɔːɹ tʃˈɪldɹən.
+DUMMY2/p244/p244_242.wav|78|hiː ɪz ɐ mˈæn ʌvðə pˈiːpəl.
+DUMMY2/p376/p376_187.wav|71|"ɪt ɪz ɐ tˈɛɹəbəl lˈɔs."
+DUMMY2/p239/p239_156.wav|48|ɪt ɪz ɐ ɡˈʊd lˈaɪfstaɪl.
+DUMMY2/p307/p307_037.wav|22|hiː ɹɪlˈiːsd ɐ hˈæfdˈʌzən sˈoʊloʊ ˈælbəmz.
+DUMMY2/p305/p305_185.wav|54|ˈaɪ æm nˌɑːt ˈiːvən θˈɪŋkɪŋ ɐbˌaʊt ðˈæt.
+DUMMY2/p272/p272_081.wav|69|ɪt wʌz mˈædʒɪk.
+DUMMY2/p302/p302_297.wav|30|aɪm tɹˈaɪɪŋ tə stˈeɪ ˈoʊpən ˌɑːn ðˈæt.
+DUMMY2/p275/p275_320.wav|40|wiː ɑːɹ ɪnðɪ ˈɛnd ɡˈeɪm.
+DUMMY2/p239/p239_231.wav|48|ðˈɛn wiː wɪl fˈeɪs ðə dˈeɪnɪʃ tʃˈæmpiənz.
+DUMMY2/p268/p268_301.wav|87|ɪt wʌz ˈoʊnli lˈeɪɾɚ ðætðə kəndˈɪʃən wʌz dˌaɪəɡnˈoʊzd.
+DUMMY2/p336/p336_088.wav|98|ðeɪ fˈeɪld tə ɹˈiːtʃ ɐɡɹˈiːmənt jˈɛstɚdˌeɪ.
+DUMMY2/p278/p278_255.wav|10|ðeɪ mˌeɪd sˈʌtʃ dᵻsˈɪʒənz ɪn lˈʌndən.
+DUMMY2/p361/p361_132.wav|79|ðæt ɡɑːt mˌiː ˈaʊt.
+DUMMY2/p307/p307_146.wav|22|juː hˈoʊp hiː pɹɪvˈeɪlz.
+DUMMY2/p244/p244_147.wav|78|ðeɪ kʊd nˌɑːt ɪɡnˈoːɹ ðə wɪl ʌv pˈɑːɹləmənt, hiː klˈeɪmd.
+DUMMY2/p294/p294_283.wav|104|ðɪs ɪz ˌaʊɚɹ ʌnfˈɪnɪʃt bˈɪznəs.
+DUMMY2/p283/p283_300.wav|95|ˈaɪ wʊdhɐv ðə hˈæmɚɹ ɪnðə kɹˈaʊd.
+DUMMY2/p239/p239_079.wav|48|ˈaɪ kæn ˌʌndɚstˈænd ðə fɹʌstɹˈeɪʃənz ʌv ˌaʊɚ fˈænz.
+DUMMY2/p264/p264_009.wav|65|ðɛɹˈɪz , ɐkˈoːɹdɪŋ tə lˈɛdʒənd, ɐ bˈɔɪlɪŋ pˈɑːt ʌv ɡˈoʊld æt wˈʌn ˈɛnd. 
+DUMMY2/p307/p307_348.wav|22|hiː dɪdnˌɑːt əpˈoʊz ðə dɪvˈoːɹs.
+DUMMY2/p304/p304_308.wav|72|wiː ɑːɹ ðə ɡˈeɪtweɪ tə dʒˈʌstɪs.
+DUMMY2/p281/p281_056.wav|36|nˈʌn hɐz ˈɛvɚ bˌɪn fˈaʊnd.
+DUMMY2/p267/p267_158.wav|0|wiː wɜː ɡˈɪvən ɐ wˈɔːɹm ænd fɹˈɛndli ɹɪsˈɛpʃən.
+DUMMY2/p300/p300_169.wav|102|hˌuː dˈuː ðiːz pˈiːpəl θˈɪŋk ðeɪ ɑːɹ?
+DUMMY2/p276/p276_177.wav|106|ðeɪ ɛɡzˈɪst ɪn nˈeɪm ɐlˈoʊn.
+DUMMY2/p228/p228_245.wav|57|ɪt ɪz ɐ pˈɑːlɪsi wˌɪtʃ hɐz ðə fˈʊl səpˈoːɹt ʌvðə mˈɪnɪstɚ.
+DUMMY2/p300/p300_303.wav|102|aɪm wˈʌndɚɹɪŋ wˌʌt juː fˈiːl ɐbˌaʊt ðə jˈʌŋɡəst.
+DUMMY2/p362/p362_247.wav|15|ðɪs wʊd ɡˈɪv skˈɑːtlənd ɐɹˈaʊnd ˈeɪt mˈɛmbɚz.
+DUMMY2/p326/p326_031.wav|28|juːnˈaɪɾᵻd wɜːɹ ɪn kəntɹˈoʊl wɪðˌaʊt ˈɔːlweɪz bˌiːɪŋ dˈɑːmɪnənt.
+DUMMY2/p361/p361_288.wav|79|ˈaɪ dɪdnˌɑːt θˈɪŋk ɪt wʌz vˈɛɹi pɹˈɑːpɚ.
+DUMMY2/p286/p286_145.wav|63|tˈaɪɡɚɹ ɪz nˌɑːt ðə nˈɔːɹm.
+DUMMY2/p234/p234_071.wav|3|ʃiː dˈɪd ðæt fɚðə ɹˈɛst ʌv hɜː lˈaɪf.
+DUMMY2/p263/p263_296.wav|39|ðə dᵻsˈɪʒən wʌz ɐnˈaʊnst æt ɪts ˈænjuːəl kˈɑːnfɹəns ɪn dˈʌnfɚmlˌaɪn.
+DUMMY2/p323/p323_228.wav|34|ʃiː bɪkˌeɪm ɐ hˈɛɹoʊˌɪn ʌv maɪ tʃˈaɪldhʊd.
+DUMMY2/p280/p280_346.wav|52|ɪt wʌzɐ bˈɪt lˈaɪk hˌævɪŋ tʃˈɪldɹən.
+DUMMY2/p333/p333_080.wav|64|bˌʌt ðə tɹˈædʒədi dɪdnˌɑːt stˈɑːp ðˈɛɹ.
+DUMMY2/p226/p226_268.wav|43|ðæt dᵻsˈɪʒən ɪz fɚðə bɹˈɪɾɪʃ pˈɑːɹləmənt ænd pˈiːpəl.
+DUMMY2/p362/p362_314.wav|15|ɪz ðæt ɹˈaɪt?
+DUMMY2/p240/p240_047.wav|93|ɪt ɪz sˌoʊ sˈæd.
+DUMMY2/p250/p250_207.wav|24|juː kʊd fˈiːl ðə hˈiːt.
+DUMMY2/p273/p273_176.wav|56|nˈiːðɚ sˈaɪd wʊd ɹɪvˈiːl ðə diːtˈeɪlz ʌvðɪ ˈɑːfɚ.
+DUMMY2/p316/p316_147.wav|85|ænd fɹˈæŋkli, ɪts bˌɪn ɐ wˈaɪl.
+DUMMY2/p265/p265_047.wav|73|ɪt ɪz juːnˈiːk.
+DUMMY2/p336/p336_353.wav|98|sˈʌmtaɪmz juː ɡˈɛt ðˌɛm, sˈʌmtaɪmz juː dˈoʊnt.
+DUMMY2/p230/p230_376.wav|35|ðɪs hˈæzənt hˈæpənd ɪn ɐ vˈækjuːm.
+DUMMY2/p308/p308_209.wav|107|ðɛɹ ɪz ɡɹˈeɪt pətˈɛnʃəl ˌɑːn ðɪs ɹˈɪvɚ.
+DUMMY2/p250/p250_442.wav|24|wiː hɐvnˌɑːt jˈɛt ɹɪsˈiːvd ɐ lˈɛɾɚ fɹʌmðɪ ˈaɪɹɪʃ.
+DUMMY2/p260/p260_037.wav|81|ɪts ɐ fˈækt.
+DUMMY2/p299/p299_345.wav|58|wɪɹ vˈɛɹi ɛksˈaɪɾᵻd ænd tʃˈælɪndʒd baɪ ðə pɹˈɑːdʒɛkt.
+DUMMY2/p269/p269_218.wav|94|ɐ ɡɹˈæmpiən pəlˈiːs spˈoʊksmən sˈɛd.
+DUMMY2/p306/p306_014.wav|12|tə ðə hˈiːbɹuːz ɪt wʌzɐ tˈoʊkən ðæt ðɛɹ wʊd biː nˈoʊmˌoːɹ jˌuːnɪvˈɜːsəl flˈʌdz.
+DUMMY2/p271/p271_292.wav|27|ɪts ɐ ɹˈɛkɚd lˈeɪbəl, nˌɑːɾə fˈɔːɹm ʌv mjˈuːzɪk.
+DUMMY2/p247/p247_225.wav|14|ˈaɪ æm kənsˈɪdɚd ɐ tˈiːneɪdʒɚ.
+DUMMY2/p294/p294_094.wav|104|ɪt ʃˌʊd biː ɐ kəndˈɪʃən ʌv ɛmplˈɔɪmənt.
+DUMMY2/p269/p269_031.wav|94|ɪz ðɪs ˈækjʊɹət?
+DUMMY2/p275/p275_116.wav|40|ɪts nˌɑːt fˈɛɹ.
+DUMMY2/p265/p265_006.wav|73|wˌɛn ðə sˈʌnlaɪt stɹˈaɪks ɹˈeɪndɹɑːps ɪnðɪ ˈɛɹ, ðeɪ ˈækt æz ɐ pɹˈɪzəm ænd fˈɔːɹm ɐ ɹˈeɪnboʊ.
+DUMMY2/p285/p285_072.wav|2|mˈɪstɚɹ ˈɜːvaɪn sˈɛd mˈɪstɚ ɹˈæfɚɾi wʌz nˈaʊ ɪn ɡˈʊd spˈɪɹɪts.
+DUMMY2/p270/p270_167.wav|8|wiː dˈɪd wˌʌt wiː hædtə dˈuː.
+DUMMY2/p360/p360_397.wav|60|ɪt ɪz ɐ ɹɪlˈiːf.

vits/inference.ipynb

@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import IPython.display as ipd\n",
+    "\n",
+    "import os\n",
+    "import json\n",
+    "import math\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch.nn import functional as F\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "import commons\n",
+    "import utils\n",
+    "from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate\n",
+    "from models import SynthesizerTrn\n",
+    "from text.symbols import symbols\n",
+    "from text import text_to_sequence\n",
+    "\n",
+    "from scipy.io.wavfile import write\n",
+    "\n",
+    "\n",
+    "def get_text(text, hps):\n",
+    "    text_norm = text_to_sequence(text, hps.data.text_cleaners)\n",
+    "    if hps.data.add_blank:\n",
+    "        text_norm = commons.intersperse(text_norm, 0)\n",
+    "    text_norm = torch.LongTensor(text_norm)\n",
+    "    return text_norm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LJ Speech"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hps = utils.get_hparams_from_file(\"./configs/ljs_base.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_g = SynthesizerTrn(\n",
+    "    len(symbols),\n",
+    "    hps.data.filter_length // 2 + 1,\n",
+    "    hps.train.segment_size // hps.data.hop_length,\n",
+    "    **hps.model).cuda()\n",
+    "_ = net_g.eval()\n",
+    "\n",
+    "_ = utils.load_checkpoint(\"/path/to/pretrained_ljs.pth\", net_g, None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stn_tst = get_text(\"VITS is Awesome!\", hps)\n",
+    "with torch.no_grad():\n",
+    "    x_tst = stn_tst.cuda().unsqueeze(0)\n",
+    "    x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
+    "    audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()\n",
+    "ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VCTK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hps = utils.get_hparams_from_file(\"./configs/vctk_base.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_g = SynthesizerTrn(\n",
+    "    len(symbols),\n",
+    "    hps.data.filter_length // 2 + 1,\n",
+    "    hps.train.segment_size // hps.data.hop_length,\n",
+    "    n_speakers=hps.data.n_speakers,\n",
+    "    **hps.model).cuda()\n",
+    "_ = net_g.eval()\n",
+    "\n",
+    "_ = utils.load_checkpoint(\"/path/to/pretrained_vctk.pth\", net_g, None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stn_tst = get_text(\"VITS is Awesome!\", hps)\n",
+    "with torch.no_grad():\n",
+    "    x_tst = stn_tst.cuda().unsqueeze(0)\n",
+    "    x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
+    "    sid = torch.LongTensor([4]).cuda()\n",
+    "    audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()\n",
+    "ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Voice Conversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)\n",
+    "collate_fn = TextAudioSpeakerCollate()\n",
+    "loader = DataLoader(dataset, num_workers=8, shuffle=False,\n",
+    "    batch_size=1, pin_memory=True,\n",
+    "    drop_last=True, collate_fn=collate_fn)\n",
+    "data_list = list(loader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda() for x in data_list[0]]\n",
+    "    sid_tgt1 = torch.LongTensor([1]).cuda()\n",
+    "    sid_tgt2 = torch.LongTensor([2]).cuda()\n",
+    "    sid_tgt3 = torch.LongTensor([4]).cuda()\n",
+    "    audio1 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0,0].data.cpu().float().numpy()\n",
+    "    audio2 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt2)[0][0,0].data.cpu().float().numpy()\n",
+    "    audio3 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt3)[0][0,0].data.cpu().float().numpy()\n",
+    "print(\"Original SID: %d\" % sid_src.item())\n",
+    "ipd.display(ipd.Audio(y[0].cpu().numpy(), rate=hps.data.sampling_rate, normalize=False))\n",
+    "print(\"Converted SID: %d\" % sid_tgt1.item())\n",
+    "ipd.display(ipd.Audio(audio1, rate=hps.data.sampling_rate, normalize=False))\n",
+    "print(\"Converted SID: %d\" % sid_tgt2.item())\n",
+    "ipd.display(ipd.Audio(audio2, rate=hps.data.sampling_rate, normalize=False))\n",
+    "print(\"Converted SID: %d\" % sid_tgt3.item())\n",
+    "ipd.display(ipd.Audio(audio3, rate=hps.data.sampling_rate, normalize=False))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

vits/losses.py

@@ -0,0 +1,61 @@
+import torch 
+from torch.nn import functional as F
+
+import commons
+
+
+def feature_loss(fmap_r, fmap_g):
+  loss = 0
+  for dr, dg in zip(fmap_r, fmap_g):
+    for rl, gl in zip(dr, dg):
+      rl = rl.float().detach()
+      gl = gl.float()
+      loss += torch.mean(torch.abs(rl - gl))
+
+  return loss * 2 
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+  loss = 0
+  r_losses = []
+  g_losses = []
+  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+    dr = dr.float()
+    dg = dg.float()
+    r_loss = torch.mean((1-dr)**2)
+    g_loss = torch.mean(dg**2)
+    loss += (r_loss + g_loss)
+    r_losses.append(r_loss.item())
+    g_losses.append(g_loss.item())
+
+  return loss, r_losses, g_losses
+
+
+def generator_loss(disc_outputs):
+  loss = 0
+  gen_losses = []
+  for dg in disc_outputs:
+    dg = dg.float()
+    l = torch.mean((1-dg)**2)
+    gen_losses.append(l)
+    loss += l
+
+  return loss, gen_losses
+
+
+def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
+  """
+  z_p, logs_q: [b, h, t_t]
+  m_p, logs_p: [b, h, t_t]
+  """
+  z_p = z_p.float()
+  logs_q = logs_q.float()
+  m_p = m_p.float()
+  logs_p = logs_p.float()
+  z_mask = z_mask.float()
+
+  kl = logs_p - logs_q - 0.5
+  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
+  kl = torch.sum(kl * z_mask)
+  l = kl / torch.sum(z_mask)
+  return l

vits/mel_processing.py

@@ -0,0 +1,112 @@
+import math
+import os
+import random
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.utils.data
+import numpy as np
+import librosa
+import librosa.util as librosa_util
+from librosa.util import normalize, pad_center, tiny
+from scipy.signal import get_window
+from scipy.io.wavfile import read
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + '_' + str(spec.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec
+
+
+def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global mel_basis, hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec

vits/models.py

@@ -0,0 +1,534 @@
+import copy
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+import attentions
+import monotonic_align
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+
+
+class StochasticDurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+    super().__init__()
+    filter_channels = in_channels # it needs to be removed from future version.
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.log_flow = modules.Log()
+    self.flows = nn.ModuleList()
+    self.flows.append(modules.ElementwiseAffine(2))
+    for i in range(n_flows):
+      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.flows.append(modules.Flip())
+
+    self.post_pre = nn.Conv1d(1, filter_channels, 1)
+    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    self.post_flows = nn.ModuleList()
+    self.post_flows.append(modules.ElementwiseAffine(2))
+    for i in range(4):
+      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.post_flows.append(modules.Flip())
+
+    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+    x = torch.detach(x)
+    x = self.pre(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.convs(x, x_mask)
+    x = self.proj(x) * x_mask
+
+    if not reverse:
+      flows = self.flows
+      assert w is not None
+
+      logdet_tot_q = 0 
+      h_w = self.post_pre(w)
+      h_w = self.post_convs(h_w, x_mask)
+      h_w = self.post_proj(h_w) * x_mask
+      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+      z_q = e_q
+      for flow in self.post_flows:
+        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+        logdet_tot_q += logdet_q
+      z_u, z1 = torch.split(z_q, [1, 1], 1) 
+      u = torch.sigmoid(z_u) * x_mask
+      z0 = (w - u) * x_mask
+      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
+      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
+
+      logdet_tot = 0
+      z0, logdet = self.log_flow(z0, x_mask)
+      logdet_tot += logdet
+      z = torch.cat([z0, z1], 1)
+      for flow in flows:
+        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+        logdet_tot = logdet_tot + logdet
+      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
+      return nll + logq # [b]
+    else:
+      flows = list(reversed(self.flows))
+      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
+      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+      for flow in flows:
+        z = flow(z, x_mask, g=x, reverse=reverse)
+      z0, z1 = torch.split(z, [1, 1], 1)
+      logw = z0
+      return logw
+
+
+class DurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+    super().__init__()
+
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.gin_channels = gin_channels
+
+    self.drop = nn.Dropout(p_dropout)
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_1 = modules.LayerNorm(filter_channels)
+    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_2 = modules.LayerNorm(filter_channels)
+    self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+  def forward(self, x, x_mask, g=None):
+    x = torch.detach(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.conv_1(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_1(x)
+    x = self.drop(x)
+    x = self.conv_2(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_2(x)
+    x = self.drop(x)
+    x = self.proj(x * x_mask)
+    return x * x_mask
+
+
+class TextEncoder(nn.Module):
+  def __init__(self,
+      n_vocab,
+      out_channels,
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout):
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+
+    self.emb = nn.Embedding(n_vocab, hidden_channels)
+    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+    self.encoder = attentions.Encoder(
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout)
+    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths):
+    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
+    x = torch.transpose(x, 1, -1) # [b, h, t]
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+    x = self.encoder(x * x_mask, x_mask)
+    stats = self.proj(x) * x_mask
+
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class PosteriorEncoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self, 
+    n_vocab,
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock, 
+    resblock_kernel_sizes, 
+    resblock_dilation_sizes, 
+    upsample_rates, 
+    upsample_initial_channel, 
+    upsample_kernel_sizes,
+    n_speakers=0,
+    gin_channels=0,
+    use_sdp=True,
+    **kwargs):
+
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.n_speakers = n_speakers
+    self.gin_channels = gin_channels
+
+    self.use_sdp = use_sdp
+
+    self.enc_p = TextEncoder(n_vocab,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+    if use_sdp:
+      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
+    else:
+      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
+
+    if n_speakers > 1:
+      self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+  def forward(self, x, x_lengths, y, y_lengths, sid=None):
+
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+    z_p = self.flow(z, y_mask, g=g)
+
+    with torch.no_grad():
+      # negative cross-entropy
+      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
+      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+    w = attn.sum(2)
+    if self.use_sdp:
+      l_length = self.dp(x, x_mask, w, g=g)
+      l_length = l_length / torch.sum(x_mask)
+    else:
+      logw_ = torch.log(w + 1e-6) * x_mask
+      logw = self.dp(x, x_mask, g=g)
+      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
+
+    # expand prior
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
+
+    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    if self.use_sdp:
+      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+    else:
+      logw = self.dp(x, x_mask, g=g)
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+    attn = commons.generate_path(w_ceil, attn_mask)
+
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    z = self.flow(z_p, y_mask, g=g, reverse=True)
+    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
+    return o, attn, y_mask, (z, z_p, m_p, logs_p)
+
+  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
+    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
+    g_src = self.emb_g(sid_src).unsqueeze(-1)
+    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
+    z_p = self.flow(z, y_mask, g=g_src)
+    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
+    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
+    return o_hat, y_mask, (z, z_p, z_hat)
+

vits/modules.py

@@ -0,0 +1,390 @@
+import copy
+import math
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+import commons
+from commons import init_weights, get_padding
+from transforms import piecewise_rational_quadratic_transform
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    super().__init__()
+    self.channels = channels
+    self.eps = eps
+
+    self.gamma = nn.Parameter(torch.ones(channels))
+    self.beta = nn.Parameter(torch.zeros(channels))
+
+  def forward(self, x):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+ 
+class ConvReluNorm(nn.Module):
+  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+    super().__init__()
+    self.in_channels = in_channels
+    self.hidden_channels = hidden_channels
+    self.out_channels = out_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+    assert n_layers > 1, "Number of layers should be larger than 0."
+
+    self.conv_layers = nn.ModuleList()
+    self.norm_layers = nn.ModuleList()
+    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+    self.norm_layers.append(LayerNorm(hidden_channels))
+    self.relu_drop = nn.Sequential(
+        nn.ReLU(),
+        nn.Dropout(p_dropout))
+    for _ in range(n_layers-1):
+      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+      self.norm_layers.append(LayerNorm(hidden_channels))
+    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask):
+    x_org = x
+    for i in range(self.n_layers):
+      x = self.conv_layers[i](x * x_mask)
+      x = self.norm_layers[i](x)
+      x = self.relu_drop(x)
+    x = x_org + self.proj(x)
+    return x * x_mask
+
+
+class DDSConv(nn.Module):
+  """
+  Dialted and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    self.hidden_channels =hidden_channels
+    self.kernel_size = kernel_size,
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+    self.p_dropout = p_dropout
+
+    self.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
+      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+    for i in range(n_layers):
+      dilation = dilation_rate ** i
+      padding = int((kernel_size * dilation - dilation) / 2)
+      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
+                                 dilation=dilation, padding=padding)
+      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+      self.in_layers.append(in_layer)
+
+      # last one is not necessary
+      if i < n_layers - 1:
+        res_skip_channels = 2 * hidden_channels
+      else:
+        res_skip_channels = hidden_channels
+
+      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+      self.res_skip_layers.append(res_skip_layer)
+
+  def forward(self, x, x_mask, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      if g is not None:
+        cond_offset = i * 2 * self.hidden_channels
+        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
+      else:
+        g_l = torch.zeros_like(x_in)
+
+      acts = commons.fused_add_tanh_sigmoid_multiply(
+          x_in,
+          g_l,
+          n_channels_tensor)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * x_mask
+        output = output + res_skip_acts[:,self.hidden_channels:,:]
+      else:
+        output = output + res_skip_acts
+    return output * x_mask
+
+  def remove_weight_norm(self):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+    
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x
+
+
+class ConvFlow(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.num_bins = num_bins
+    self.tail_bound = tail_bound
+    self.half_channels = in_channels // 2
+
+    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
+    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0)
+    h = self.convs(h, x_mask, g=g)
+    h = self.proj(h) * x_mask
+
+    b, c, t = x0.shape
+    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
+
+    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_derivatives = h[..., 2 * self.num_bins:]
+
+    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
+        unnormalized_widths,
+        unnormalized_heights,
+        unnormalized_derivatives,
+        inverse=reverse,
+        tails='linear',
+        tail_bound=self.tail_bound
+    )
+
+    x = torch.cat([x0, x1], 1) * x_mask
+    logdet = torch.sum(logabsdet * x_mask, [1,2])
+    if not reverse:
+        return x, logdet
+    else:
+        return x

vits/monotonic_align/__init__.py

@@ -0,0 +1,19 @@
+import numpy as np
+import torch
+from .monotonic_align.core import maximum_path_c
+
+
+def maximum_path(neg_cent, mask):
+  """ Cython optimized version.
+  neg_cent: [b, t_t, t_s]
+  mask: [b, t_t, t_s]
+  """
+  device = neg_cent.device
+  dtype = neg_cent.dtype
+  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
+  path = np.zeros(neg_cent.shape, dtype=np.int32)
+
+  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
+  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
+  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
+  return torch.from_numpy(path).to(device=device, dtype=dtype)

vits/monotonic_align/core.pyx

@@ -0,0 +1,42 @@
+cimport cython
+from cython.parallel import prange
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
+  cdef int x
+  cdef int y
+  cdef float v_prev
+  cdef float v_cur
+  cdef float tmp
+  cdef int index = t_x - 1
+
+  for y in range(t_y):
+    for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+      if x == y:
+        v_cur = max_neg_val
+      else:
+        v_cur = value[y-1, x]
+      if x == 0:
+        if y == 0:
+          v_prev = 0.
+        else:
+          v_prev = max_neg_val
+      else:
+        v_prev = value[y-1, x-1]
+      value[y, x] += max(v_prev, v_cur)
+
+  for y in range(t_y - 1, -1, -1):
+    path[y, index] = 1
+    if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
+      index = index - 1
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
+  cdef int b = paths.shape[0]
+  cdef int i
+  for i in prange(b, nogil=True):
+    maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])

vits/monotonic_align/setup.py

@@ -0,0 +1,9 @@
+from distutils.core import setup
+from Cython.Build import cythonize
+import numpy
+
+setup(
+  name = 'monotonic_align',
+  ext_modules = cythonize("core.pyx"),
+  include_dirs=[numpy.get_include()]
+)

vits/preprocess.py

@@ -0,0 +1,25 @@
+import argparse
+import text
+from utils import load_filepaths_and_text
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--out_extension", default="cleaned")
+  parser.add_argument("--text_index", default=1, type=int)
+  parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
+  parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
+
+  args = parser.parse_args()
+    
+
+  for filelist in args.filelists:
+    print("START:", filelist)
+    filepaths_and_text = load_filepaths_and_text(filelist)
+    for i in range(len(filepaths_and_text)):
+      original_text = filepaths_and_text[i][args.text_index]
+      cleaned_text = text._clean_text(original_text, args.text_cleaners)
+      filepaths_and_text[i][args.text_index] = cleaned_text
+
+    new_filelist = filelist + "." + args.out_extension
+    with open(new_filelist, "w", encoding="utf-8") as f:
+      f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])

vits/requirements.txt

@@ -0,0 +1,10 @@
+Cython==0.29.21
+librosa==0.8.0
+matplotlib==3.3.1
+numpy==1.18.5
+phonemizer==2.2.1
+scipy==1.5.2
+tensorboard==2.3.0
+torch==1.6.0
+torchvision==0.7.0
+Unidecode==1.1.1

vits/text/LICENSE

@@ -0,0 +1,19 @@
+Copyright (c) 2017 Keith Ito
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

vits/text/__init__.py

@@ -0,0 +1,54 @@
+""" from https://github.com/keithito/tacotron """
+from text import cleaners
+from text.symbols import symbols
+
+
+# Mappings from symbol to numeric ID and vice versa:
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+
+def text_to_sequence(text, cleaner_names):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+      cleaner_names: names of the cleaner functions to run the text through
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  sequence = []
+
+  clean_text = _clean_text(text, cleaner_names)
+  for symbol in clean_text:
+    symbol_id = _symbol_to_id[symbol]
+    sequence += [symbol_id]
+  return sequence
+
+
+def cleaned_text_to_sequence(cleaned_text):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text]
+  return sequence
+
+
+def sequence_to_text(sequence):
+  '''Converts a sequence of IDs back to a string'''
+  result = ''
+  for symbol_id in sequence:
+    s = _id_to_symbol[symbol_id]
+    result += s
+  return result
+
+
+def _clean_text(text, cleaner_names):
+  for name in cleaner_names:
+    cleaner = getattr(cleaners, name)
+    if not cleaner:
+      raise Exception('Unknown cleaner: %s' % name)
+    text = cleaner(text)
+  return text

vits/text/cleaners.py

@@ -0,0 +1,100 @@
+""" from https://github.com/keithito/tacotron """
+
+'''
+Cleaners are transformations that run over the input text at both training and eval time.
+
+Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
+hyperparameter. Some cleaners are English-specific. You'll typically want to use:
+  1. "english_cleaners" for English text
+  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
+     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
+  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
+     the symbols in symbols.py to match your data).
+'''
+
+import re
+from unidecode import unidecode
+from phonemizer import phonemize
+
+
+# Regular expression matching whitespace:
+_whitespace_re = re.compile(r'\s+')
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('mrs', 'misess'),
+  ('mr', 'mister'),
+  ('dr', 'doctor'),
+  ('st', 'saint'),
+  ('co', 'company'),
+  ('jr', 'junior'),
+  ('maj', 'major'),
+  ('gen', 'general'),
+  ('drs', 'doctors'),
+  ('rev', 'reverend'),
+  ('lt', 'lieutenant'),
+  ('hon', 'honorable'),
+  ('sgt', 'sergeant'),
+  ('capt', 'captain'),
+  ('esq', 'esquire'),
+  ('ltd', 'limited'),
+  ('col', 'colonel'),
+  ('ft', 'fort'),
+]]
+
+
+def expand_abbreviations(text):
+  for regex, replacement in _abbreviations:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def expand_numbers(text):
+  return normalize_numbers(text)
+
+
+def lowercase(text):
+  return text.lower()
+
+
+def collapse_whitespace(text):
+  return re.sub(_whitespace_re, ' ', text)
+
+
+def convert_to_ascii(text):
+  return unidecode(text)
+
+
+def basic_cleaners(text):
+  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def transliteration_cleaners(text):
+  '''Pipeline for non-English text that transliterates to ASCII.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def english_cleaners(text):
+  '''Pipeline for English text, including abbreviation expansion.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = expand_abbreviations(text)
+  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True)
+  phonemes = collapse_whitespace(phonemes)
+  return phonemes
+
+
+def english_cleaners2(text):
+  '''Pipeline for English text, including abbreviation expansion. + punctuation + stress'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = expand_abbreviations(text)
+  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True, preserve_punctuation=True, with_stress=True)
+  phonemes = collapse_whitespace(phonemes)
+  return phonemes

vits/text/symbols.py

@@ -0,0 +1,16 @@
+""" from https://github.com/keithito/tacotron """
+
+'''
+Defines the set of symbols used in text input to the model.
+'''
+_pad        = '_'
+_punctuation = ';:,.!?¡¿—…"«»“” '
+_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
+_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
+
+
+# Export all symbols:
+symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
+
+# Special symbol ids
+SPACE_ID = symbols.index(" ")

vits/train.py

@@ -0,0 +1,290 @@
+import os
+import json
+import argparse
+import itertools
+import math
+import torch
+from torch import nn, optim
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+
+import commons
+import utils
+from data_utils import (
+  TextAudioLoader,
+  TextAudioCollate,
+  DistributedBucketSampler
+)
+from models import (
+  SynthesizerTrn,
+  MultiPeriodDiscriminator,
+)
+from losses import (
+  generator_loss,
+  discriminator_loss,
+  feature_loss,
+  kl_loss
+)
+from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from text.symbols import symbols
+
+
+torch.backends.cudnn.benchmark = True
+global_step = 0
+
+
+def main():
+  """Assume Single Node Multi GPUs Training Only"""
+  assert torch.cuda.is_available(), "CPU training is not allowed."
+
+  n_gpus = torch.cuda.device_count()
+  os.environ['MASTER_ADDR'] = 'localhost'
+  os.environ['MASTER_PORT'] = '80000'
+
+  hps = utils.get_hparams()
+  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
+
+
+def run(rank, n_gpus, hps):
+  global global_step
+  if rank == 0:
+    logger = utils.get_logger(hps.model_dir)
+    logger.info(hps)
+    utils.check_git_hash(hps.model_dir)
+    writer = SummaryWriter(log_dir=hps.model_dir)
+    writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
+
+  dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
+  torch.manual_seed(hps.train.seed)
+  torch.cuda.set_device(rank)
+
+  train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
+  train_sampler = DistributedBucketSampler(
+      train_dataset,
+      hps.train.batch_size,
+      [32,300,400,500,600,700,800,900,1000],
+      num_replicas=n_gpus,
+      rank=rank,
+      shuffle=True)
+  collate_fn = TextAudioCollate()
+  train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
+      collate_fn=collate_fn, batch_sampler=train_sampler)
+  if rank == 0:
+    eval_dataset = TextAudioLoader(hps.data.validation_files, hps.data)
+    eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
+        batch_size=hps.train.batch_size, pin_memory=True,
+        drop_last=False, collate_fn=collate_fn)
+
+  net_g = SynthesizerTrn(
+      len(symbols),
+      hps.data.filter_length // 2 + 1,
+      hps.train.segment_size // hps.data.hop_length,
+      **hps.model).cuda(rank)
+  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
+  optim_g = torch.optim.AdamW(
+      net_g.parameters(), 
+      hps.train.learning_rate, 
+      betas=hps.train.betas, 
+      eps=hps.train.eps)
+  optim_d = torch.optim.AdamW(
+      net_d.parameters(),
+      hps.train.learning_rate, 
+      betas=hps.train.betas, 
+      eps=hps.train.eps)
+  net_g = DDP(net_g, device_ids=[rank])
+  net_d = DDP(net_d, device_ids=[rank])
+
+  try:
+    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
+    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
+    global_step = (epoch_str - 1) * len(train_loader)
+  except:
+    epoch_str = 1
+    global_step = 0
+
+  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
+  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
+
+  scaler = GradScaler(enabled=hps.train.fp16_run)
+
+  for epoch in range(epoch_str, hps.train.epochs + 1):
+    if rank==0:
+      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
+    else:
+      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
+    scheduler_g.step()
+    scheduler_d.step()
+
+
+def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
+  net_g, net_d = nets
+  optim_g, optim_d = optims
+  scheduler_g, scheduler_d = schedulers
+  train_loader, eval_loader = loaders
+  if writers is not None:
+    writer, writer_eval = writers
+
+  train_loader.batch_sampler.set_epoch(epoch)
+  global global_step
+
+  net_g.train()
+  net_d.train()
+  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(train_loader):
+    x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
+    spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
+    y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
+
+    with autocast(enabled=hps.train.fp16_run):
+      y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
+      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths)
+
+      mel = spec_to_mel_torch(
+          spec, 
+          hps.data.filter_length, 
+          hps.data.n_mel_channels, 
+          hps.data.sampling_rate,
+          hps.data.mel_fmin, 
+          hps.data.mel_fmax)
+      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
+      y_hat_mel = mel_spectrogram_torch(
+          y_hat.squeeze(1), 
+          hps.data.filter_length, 
+          hps.data.n_mel_channels, 
+          hps.data.sampling_rate, 
+          hps.data.hop_length, 
+          hps.data.win_length, 
+          hps.data.mel_fmin, 
+          hps.data.mel_fmax
+      )
+
+      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice 
+
+      # Discriminator
+      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+      with autocast(enabled=False):
+        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
+        loss_disc_all = loss_disc
+    optim_d.zero_grad()
+    scaler.scale(loss_disc_all).backward()
+    scaler.unscale_(optim_d)
+    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
+    scaler.step(optim_d)
+
+    with autocast(enabled=hps.train.fp16_run):
+      # Generator
+      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+      with autocast(enabled=False):
+        loss_dur = torch.sum(l_length.float())
+        loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+        loss_fm = feature_loss(fmap_r, fmap_g)
+        loss_gen, losses_gen = generator_loss(y_d_hat_g)
+        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
+    optim_g.zero_grad()
+    scaler.scale(loss_gen_all).backward()
+    scaler.unscale_(optim_g)
+    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
+    scaler.step(optim_g)
+    scaler.update()
+
+    if rank==0:
+      if global_step % hps.train.log_interval == 0:
+        lr = optim_g.param_groups[0]['lr']
+        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
+        logger.info('Train Epoch: {} [{:.0f}%]'.format(
+          epoch,
+          100. * batch_idx / len(train_loader)))
+        logger.info([x.item() for x in losses] + [global_step, lr])
+        
+        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
+        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
+
+        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
+        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
+        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
+        image_dict = { 
+            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
+            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), 
+            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
+            "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
+        }
+        utils.summarize(
+          writer=writer,
+          global_step=global_step, 
+          images=image_dict,
+          scalars=scalar_dict)
+
+      if global_step % hps.train.eval_interval == 0:
+        evaluate(hps, net_g, eval_loader, writer_eval)
+        utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
+        utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
+    global_step += 1
+  
+  if rank == 0:
+    logger.info('====> Epoch: {}'.format(epoch))
+
+ 
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    with torch.no_grad():
+      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(eval_loader):
+        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
+        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
+        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
+
+        # remove else
+        x = x[:1]
+        x_lengths = x_lengths[:1]
+        spec = spec[:1]
+        spec_lengths = spec_lengths[:1]
+        y = y[:1]
+        y_lengths = y_lengths[:1]
+        break
+      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, max_len=1000)
+      y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
+
+      mel = spec_to_mel_torch(
+        spec, 
+        hps.data.filter_length, 
+        hps.data.n_mel_channels, 
+        hps.data.sampling_rate,
+        hps.data.mel_fmin, 
+        hps.data.mel_fmax)
+      y_hat_mel = mel_spectrogram_torch(
+        y_hat.squeeze(1).float(),
+        hps.data.filter_length,
+        hps.data.n_mel_channels,
+        hps.data.sampling_rate,
+        hps.data.hop_length,
+        hps.data.win_length,
+        hps.data.mel_fmin,
+        hps.data.mel_fmax
+      )
+    image_dict = {
+      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
+    }
+    audio_dict = {
+      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
+    }
+    if global_step == 0:
+      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
+      audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
+
+    utils.summarize(
+      writer=writer_eval,
+      global_step=global_step, 
+      images=image_dict,
+      audios=audio_dict,
+      audio_sampling_rate=hps.data.sampling_rate
+    )
+    generator.train()
+
+                           
+if __name__ == "__main__":
+  main()

vits/train_ms.py

@@ -0,0 +1,294 @@
+import os
+import json
+import argparse
+import itertools
+import math
+import torch
+from torch import nn, optim
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+
+import commons
+import utils
+from data_utils import (
+  TextAudioSpeakerLoader,
+  TextAudioSpeakerCollate,
+  DistributedBucketSampler
+)
+from models import (
+  SynthesizerTrn,
+  MultiPeriodDiscriminator,
+)
+from losses import (
+  generator_loss,
+  discriminator_loss,
+  feature_loss,
+  kl_loss
+)
+from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from text.symbols import symbols
+
+
+torch.backends.cudnn.benchmark = True
+global_step = 0
+
+
+def main():
+  """Assume Single Node Multi GPUs Training Only"""
+  assert torch.cuda.is_available(), "CPU training is not allowed."
+
+  n_gpus = torch.cuda.device_count()
+  os.environ['MASTER_ADDR'] = 'localhost'
+  os.environ['MASTER_PORT'] = '80000'
+
+  hps = utils.get_hparams()
+  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
+
+
+def run(rank, n_gpus, hps):
+  global global_step
+  if rank == 0:
+    logger = utils.get_logger(hps.model_dir)
+    logger.info(hps)
+    utils.check_git_hash(hps.model_dir)
+    writer = SummaryWriter(log_dir=hps.model_dir)
+    writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
+
+  dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
+  torch.manual_seed(hps.train.seed)
+  torch.cuda.set_device(rank)
+
+  train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
+  train_sampler = DistributedBucketSampler(
+      train_dataset,
+      hps.train.batch_size,
+      [32,300,400,500,600,700,800,900,1000],
+      num_replicas=n_gpus,
+      rank=rank,
+      shuffle=True)
+  collate_fn = TextAudioSpeakerCollate()
+  train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
+      collate_fn=collate_fn, batch_sampler=train_sampler)
+  if rank == 0:
+    eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
+    eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
+        batch_size=hps.train.batch_size, pin_memory=True,
+        drop_last=False, collate_fn=collate_fn)
+
+  net_g = SynthesizerTrn(
+      len(symbols),
+      hps.data.filter_length // 2 + 1,
+      hps.train.segment_size // hps.data.hop_length,
+      n_speakers=hps.data.n_speakers,
+      **hps.model).cuda(rank)
+  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
+  optim_g = torch.optim.AdamW(
+      net_g.parameters(), 
+      hps.train.learning_rate, 
+      betas=hps.train.betas, 
+      eps=hps.train.eps)
+  optim_d = torch.optim.AdamW(
+      net_d.parameters(),
+      hps.train.learning_rate, 
+      betas=hps.train.betas, 
+      eps=hps.train.eps)
+  net_g = DDP(net_g, device_ids=[rank])
+  net_d = DDP(net_d, device_ids=[rank])
+
+  try:
+    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
+    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
+    global_step = (epoch_str - 1) * len(train_loader)
+  except:
+    epoch_str = 1
+    global_step = 0
+
+  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
+  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
+
+  scaler = GradScaler(enabled=hps.train.fp16_run)
+
+  for epoch in range(epoch_str, hps.train.epochs + 1):
+    if rank==0:
+      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
+    else:
+      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
+    scheduler_g.step()
+    scheduler_d.step()
+
+
+def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
+  net_g, net_d = nets
+  optim_g, optim_d = optims
+  scheduler_g, scheduler_d = schedulers
+  train_loader, eval_loader = loaders
+  if writers is not None:
+    writer, writer_eval = writers
+
+  train_loader.batch_sampler.set_epoch(epoch)
+  global global_step
+
+  net_g.train()
+  net_d.train()
+  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(train_loader):
+    x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
+    spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
+    y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
+    speakers = speakers.cuda(rank, non_blocking=True)
+
+    with autocast(enabled=hps.train.fp16_run):
+      y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
+      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)
+
+      mel = spec_to_mel_torch(
+          spec, 
+          hps.data.filter_length, 
+          hps.data.n_mel_channels, 
+          hps.data.sampling_rate,
+          hps.data.mel_fmin, 
+          hps.data.mel_fmax)
+      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
+      y_hat_mel = mel_spectrogram_torch(
+          y_hat.squeeze(1), 
+          hps.data.filter_length, 
+          hps.data.n_mel_channels, 
+          hps.data.sampling_rate, 
+          hps.data.hop_length, 
+          hps.data.win_length, 
+          hps.data.mel_fmin, 
+          hps.data.mel_fmax
+      )
+
+      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice 
+
+      # Discriminator
+      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+      with autocast(enabled=False):
+        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
+        loss_disc_all = loss_disc
+    optim_d.zero_grad()
+    scaler.scale(loss_disc_all).backward()
+    scaler.unscale_(optim_d)
+    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
+    scaler.step(optim_d)
+
+    with autocast(enabled=hps.train.fp16_run):
+      # Generator
+      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+      with autocast(enabled=False):
+        loss_dur = torch.sum(l_length.float())
+        loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+        loss_fm = feature_loss(fmap_r, fmap_g)
+        loss_gen, losses_gen = generator_loss(y_d_hat_g)
+        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
+    optim_g.zero_grad()
+    scaler.scale(loss_gen_all).backward()
+    scaler.unscale_(optim_g)
+    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
+    scaler.step(optim_g)
+    scaler.update()
+
+    if rank==0:
+      if global_step % hps.train.log_interval == 0:
+        lr = optim_g.param_groups[0]['lr']
+        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
+        logger.info('Train Epoch: {} [{:.0f}%]'.format(
+          epoch,
+          100. * batch_idx / len(train_loader)))
+        logger.info([x.item() for x in losses] + [global_step, lr])
+        
+        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
+        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
+
+        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
+        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
+        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
+        image_dict = { 
+            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
+            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), 
+            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
+            "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
+        }
+        utils.summarize(
+          writer=writer,
+          global_step=global_step, 
+          images=image_dict,
+          scalars=scalar_dict)
+
+      if global_step % hps.train.eval_interval == 0:
+        evaluate(hps, net_g, eval_loader, writer_eval)
+        utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
+        utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
+    global_step += 1
+  
+  if rank == 0:
+    logger.info('====> Epoch: {}'.format(epoch))
+
+ 
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    with torch.no_grad():
+      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(eval_loader):
+        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
+        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
+        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
+        speakers = speakers.cuda(0)
+
+        # remove else
+        x = x[:1]
+        x_lengths = x_lengths[:1]
+        spec = spec[:1]
+        spec_lengths = spec_lengths[:1]
+        y = y[:1]
+        y_lengths = y_lengths[:1]
+        speakers = speakers[:1]
+        break
+      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, max_len=1000)
+      y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
+
+      mel = spec_to_mel_torch(
+        spec, 
+        hps.data.filter_length, 
+        hps.data.n_mel_channels, 
+        hps.data.sampling_rate,
+        hps.data.mel_fmin, 
+        hps.data.mel_fmax)
+      y_hat_mel = mel_spectrogram_torch(
+        y_hat.squeeze(1).float(),
+        hps.data.filter_length,
+        hps.data.n_mel_channels,
+        hps.data.sampling_rate,
+        hps.data.hop_length,
+        hps.data.win_length,
+        hps.data.mel_fmin,
+        hps.data.mel_fmax
+      )
+    image_dict = {
+      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
+    }
+    audio_dict = {
+      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
+    }
+    if global_step == 0:
+      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
+      audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
+
+    utils.summarize(
+      writer=writer_eval,
+      global_step=global_step, 
+      images=image_dict,
+      audios=audio_dict,
+      audio_sampling_rate=hps.data.sampling_rate
+    )
+    generator.train()
+
+                           
+if __name__ == "__main__":
+  main()

vits/transforms.py

@@ -0,0 +1,193 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(inputs, 
+                                           unnormalized_widths,
+                                           unnormalized_heights,
+                                           unnormalized_derivatives,
+                                           inverse=False,
+                                           tails=None, 
+                                           tail_bound=1.,
+                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
+
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {
+            'tails': tails,
+            'tail_bound': tail_bound
+        }
+
+    outputs, logabsdet = spline_fn(
+            inputs=inputs,
+            unnormalized_widths=unnormalized_widths,
+            unnormalized_heights=unnormalized_heights,
+            unnormalized_derivatives=unnormalized_derivatives,
+            inverse=inverse,
+            min_bin_width=min_bin_width,
+            min_bin_height=min_bin_height,
+            min_derivative=min_derivative,
+            **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(
+        inputs[..., None] >= bin_locations,
+        dim=-1
+    ) - 1
+
+
+def unconstrained_rational_quadratic_spline(inputs,
+                                            unnormalized_widths,
+                                            unnormalized_heights,
+                                            unnormalized_derivatives,
+                                            inverse=False,
+                                            tails='linear',
+                                            tail_bound=1.,
+                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == 'linear':
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError('{} tails are not implemented.'.format(tails))
+
+    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative
+    )
+
+    return outputs, logabsdet
+
+def rational_quadratic_spline(inputs,
+                              unnormalized_widths,
+                              unnormalized_heights,
+                              unnormalized_derivatives,
+                              inverse=False,
+                              left=0., right=1., bottom=0., top=1.,
+                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                              min_derivative=DEFAULT_MIN_DERIVATIVE):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError('Input to a transform is not within its domain')
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError('Minimal bin width too large for the number of bins')
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError('Minimal bin height too large for the number of bins')
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (((inputs - input_cumheights) * (input_derivatives
+                                             + input_derivatives_plus_one
+                                             - 2 * input_delta)
+              + input_heights * (input_delta - input_derivatives)))
+        b = (input_heights * input_derivatives
+             - (inputs - input_cumheights) * (input_derivatives
+                                              + input_derivatives_plus_one
+                                              - 2 * input_delta))
+        c = - input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - root).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (input_delta * theta.pow(2)
+                                     + input_derivatives * theta_one_minus_theta)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - theta).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet

vits/utils.py

@@ -0,0 +1,258 @@
+import os
+import glob
+import sys
+import argparse
+import logging
+import json
+import subprocess
+import numpy as np
+from scipy.io.wavfile import read
+import torch
+
+MATPLOTLIB_FLAG = False
+
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+logger = logging
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None):
+  assert os.path.isfile(checkpoint_path)
+  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+  iteration = checkpoint_dict['iteration']
+  learning_rate = checkpoint_dict['learning_rate']
+  if optimizer is not None:
+    optimizer.load_state_dict(checkpoint_dict['optimizer'])
+  saved_state_dict = checkpoint_dict['model']
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  new_state_dict= {}
+  for k, v in state_dict.items():
+    try:
+      new_state_dict[k] = saved_state_dict[k]
+    except:
+      logger.info("%s is not in the checkpoint" % k)
+      new_state_dict[k] = v
+  if hasattr(model, 'module'):
+    model.module.load_state_dict(new_state_dict)
+  else:
+    model.load_state_dict(new_state_dict)
+  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
+    checkpoint_path, iteration))
+  return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+  logger.info("Saving model and optimizer state at iteration {} to {}".format(
+    iteration, checkpoint_path))
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  torch.save({'model': state_dict,
+              'iteration': iteration,
+              'optimizer': optimizer.state_dict(),
+              'learning_rate': learning_rate}, checkpoint_path)
+
+
+def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
+  for k, v in scalars.items():
+    writer.add_scalar(k, v, global_step)
+  for k, v in histograms.items():
+    writer.add_histogram(k, v, global_step)
+  for k, v in images.items():
+    writer.add_image(k, v, global_step, dataformats='HWC')
+  for k, v in audios.items():
+    writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+  f_list = glob.glob(os.path.join(dir_path, regex))
+  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+  x = f_list[-1]
+  print(x)
+  return x
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+    mpl_logger = logging.getLogger('matplotlib')
+    mpl_logger.setLevel(logging.WARNING)
+  import matplotlib.pylab as plt
+  import numpy as np
+  
+  fig, ax = plt.subplots(figsize=(10,2))
+  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                  interpolation='none')
+  plt.colorbar(im, ax=ax)
+  plt.xlabel("Frames")
+  plt.ylabel("Channels")
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+    mpl_logger = logging.getLogger('matplotlib')
+    mpl_logger.setLevel(logging.WARNING)
+  import matplotlib.pylab as plt
+  import numpy as np
+
+  fig, ax = plt.subplots(figsize=(6, 4))
+  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
+                  interpolation='none')
+  fig.colorbar(im, ax=ax)
+  xlabel = 'Decoder timestep'
+  if info is not None:
+      xlabel += '\n\n' + info
+  plt.xlabel(xlabel)
+  plt.ylabel('Encoder timestep')
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
+
+
+def load_wav_to_torch(full_path):
+  sampling_rate, data = read(full_path)
+  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_filepaths_and_text(filename, split="|"):
+  with open(filename, encoding='utf-8') as f:
+    filepaths_and_text = [line.strip().split(split) for line in f]
+  return filepaths_and_text
+
+
+def get_hparams(init=True):
+  parser = argparse.ArgumentParser()
+  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
+                      help='JSON file for configuration')
+  parser.add_argument('-m', '--model', type=str, required=True,
+                      help='Model name')
+  
+  args = parser.parse_args()
+  model_dir = os.path.join("./logs", args.model)
+
+  if not os.path.exists(model_dir):
+    os.makedirs(model_dir)
+
+  config_path = args.config
+  config_save_path = os.path.join(model_dir, "config.json")
+  if init:
+    with open(config_path, "r") as f:
+      data = f.read()
+    with open(config_save_path, "w") as f:
+      f.write(data)
+  else:
+    with open(config_save_path, "r") as f:
+      data = f.read()
+  config = json.loads(data)
+  
+  hparams = HParams(**config)
+  hparams.model_dir = model_dir
+  return hparams
+
+
+def get_hparams_from_dir(model_dir):
+  config_save_path = os.path.join(model_dir, "config.json")
+  with open(config_save_path, "r") as f:
+    data = f.read()
+  config = json.loads(data)
+
+  hparams =HParams(**config)
+  hparams.model_dir = model_dir
+  return hparams
+
+
+def get_hparams_from_file(config_path):
+  with open(config_path, "r") as f:
+    data = f.read()
+  config = json.loads(data)
+
+  hparams =HParams(**config)
+  return hparams
+
+
+def check_git_hash(model_dir):
+  source_dir = os.path.dirname(os.path.realpath(__file__))
+  if not os.path.exists(os.path.join(source_dir, ".git")):
+    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
+      source_dir
+    ))
+    return
+
+  cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+  path = os.path.join(model_dir, "githash")
+  if os.path.exists(path):
+    saved_hash = open(path).read()
+    if saved_hash != cur_hash:
+      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
+        saved_hash[:8], cur_hash[:8]))
+  else:
+    open(path, "w").write(cur_hash)
+
+
+def get_logger(model_dir, filename="train.log"):
+  global logger
+  logger = logging.getLogger(os.path.basename(model_dir))
+  logger.setLevel(logging.DEBUG)
+  
+  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+  if not os.path.exists(model_dir):
+    os.makedirs(model_dir)
+  h = logging.FileHandler(os.path.join(model_dir, filename))
+  h.setLevel(logging.DEBUG)
+  h.setFormatter(formatter)
+  logger.addHandler(h)
+  return logger
+
+
+class HParams():
+  def __init__(self, **kwargs):
+    for k, v in kwargs.items():
+      if type(v) == dict:
+        v = HParams(**v)
+      self[k] = v
+    
+  def keys(self):
+    return self.__dict__.keys()
+
+  def items(self):
+    return self.__dict__.items()
+
+  def values(self):
+    return self.__dict__.values()
+
+  def __len__(self):
+    return len(self.__dict__)
+
+  def __getitem__(self, key):
+    return getattr(self, key)
+
+  def __setitem__(self, key, value):
+    return setattr(self, key, value)
+
+  def __contains__(self, key):
+    return key in self.__dict__
+
+  def __repr__(self):
+    return self.__dict__.__repr__()