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LICENSE.md

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+By downloading or using the EmoV-DB Dataset, you are agreeing to the “Non-commercial Purposes” condition. “Non-commercial Purposes” means research, teaching, scientific publication and personal experimentation. Non-commercial Purposes include use of the Dataset to perform benchmarking for purposes of academic or applied research publication. Non-commercial Purposes does not include purposes primarily intended for or directed towards commercial advantage or monetary compensation, or purposes intended for or directed towards litigation, licensing, or enforcement, even in part. EmoV-DB dataset is provided as-is, is experimental in nature, and not intended for use by, with, or for the diagnosis of human subjects for incorporation into a product.
+
+If you wish to make use of the EmoV-DB Dataset for Commercial Purpose, please contact Prof. Sarah Ostadabbas
+https://coe.northeastern.edu/people/ostadabbas-sarah/

README.md

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+# EmoV-DB
+
+# See also
+https://github.com/noetits/ICE-Talk for controllable TTS
+
+# How to use
+## Download link
+Sorted version (recommended), **new link**:
+https://openslr.org/115/
+
+old link (slow download) but gives ou the folder structure needed to use "load_emov_db()" function: 
+https://mega.nz/#F!KBp32apT!gLIgyWf9iQ-yqnWFUFuUHg
+
+Not sorted version:
+http://www.coe.neu.edu/Research/AClab/Speech%20Data/
+
+## Forced alignments
+"It is the process of taking the text transcription of an audio speech segment and determining where in time particular words occur in the speech segment." [source](http://www.voxforge.org/home/docs/faq/faq/what-is-forced-alignment)
+
+It also allows to separate verbal and non-verbal vocalizations (laughs, yawns, etc.) that are before/after the sentence. 
+Note that it might also be possible to detect non-verbal vocalizations inside sentences when they are not mixed with speech (e.g. chuckle between words) with "sil" or "spn" tokens of Montreal-Forced-Aligner. But this has not been experimented on our end.
+
+
+
+### Alignment with Montreal Forced Aligner (MFA)
+First install MFA
++ [Installation](https://montreal-forced-aligner.readthedocs.io/en/latest/installation.html)
+
+Then use the steps below. It is based on the instructions of [Phone alignment of a dataset](https://montreal-forced-aligner.readthedocs.io/en/latest/first_steps/index.html#first-steps-align-pretrained) with their acoustic and g2p models.
+To use them, you need download models as in [here](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html). In this example, we use `english_us_arpa`, but you could use their IPA model as well.
+
+In a python terminal:
+```
+from emov_mfa_alignment import Emov
+dataset = Emov()
+dataset.download()
+dataset.prepare_mfa()
+```
+
+Then in a shell terminal:
+```
+mfa align EMOV-DB/ english_us_arpa english_us_arpa EMOV
+```
+
+Then the "convert" function is the function to remove non-verbal vocalizations that would be before/after the whole sentence. It just reads the results of phone alignment and extract the start timing of the first phoneme and the end timing of the last phoneme to cut the audio and rewrite it.
+
+```
+from emov_mfa_alignment import Emov
+dataset = Emov()
+dataset.convert()
+```
+
+### Alignment with gentle
+Older alternative, performance should be less good than with MFA
+<details>
+  <summary>Click to show process</summary>
+
+1. Go to https://github.com/lowerquality/gentle
+2. Clone the repo
+3. In Getting started, use the 3rd option: .\install.sh
+4. Copy align_db.py in the repository
+5. In align_db.py, change the "path" variable so that it corresponds to the path of EmoV-DB. 
+6. Launch command "python align_db.py". You'll probably have to install some packages to make it work
+7. It should create a folder called "alignments" in the repo, with the same structure as the database, containing a json file for each sentence of the database.
+
+8. The function "get_start_end_from_json(path)" allows you to extract start and end of the computed force alignment
+9. you can play a file with function "play(path)"
+10. you can play the part of the file in which there is speech according to the forced alignment with "play_start_end(path, start, end)"
+
+</details>
+
+# Overview of data
+
+The Emotional Voices Database: Towards Controlling the Emotional Expressiveness in Voice Generation Systems
+
+- This dataset is built for the purpose of emotional speech synthesis. The transcript were based on the CMU arctic database: http://www.festvox.org/cmu_arctic/cmuarctic.data.
+
+- It includes recordings for four speakers- two males and two females.
+
+- The emotional styles are neutral, sleepiness, anger, disgust and amused. 
+
+- Each audio file is recorded in 16bits .wav format 
+
+- Spk-Je (Female, English: Neutral(417 files), Amused(222 files), Angry(523 files), Sleepy(466 files), Disgust(189 files))
+- Spk-Bea (Female, English: Neutral(373 files), Amused(309 files), Angry(317 files), Sleepy(520 files), Disgust(347 files))
+- Spk-Sa (Male, English: Neutral(493 files), Amused(501 files), Angry(468 files), Sleepy(495 files), Disgust(497 files))
+- Spk-Jsh (Male, English: Neutral(302 files), Amused(298 files), Sleepy(263 files))
+
+- File naming (audio_folder): anger_1-28_0011.wav - 1) first word (emotion style), 1-28 - annotation doc file range, Last four digit is the sentence number. 
+
+- File naming (annotation_folder): anger_1-28.TextGrid - 1) first word (emotional style), 1-28- annotation doc range
+
+# References
+A description of the database here:
+https://arxiv.org/pdf/1806.09514.pdf
+
+Please reference this paper when using this database:
+
+Bibtex:
+```
+@article{adigwe2018emotional,
+  title={The emotional voices database: Towards controlling the emotion dimension in voice generation systems},
+  author={Adigwe, Adaeze and Tits, No{\'e} and Haddad, Kevin El and Ostadabbas, Sarah and Dutoit, Thierry},
+  journal={arXiv preprint arXiv:1806.09514},
+  year={2018}
+}
+```
+
+

align_db.py

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+import os
+import gentle
+import pandas as pd
+import codecs
+import logging
+
+def on_progress(p):
+    for k,v in p.items():
+        logging.debug("%s: %s" % (k, v))
+
+# DOWNLOAD THE DB AND CHANGE THIS PATH
+path='path/to/EmoV-DB_sorted/'
+
+
+
+resources = gentle.Resources()
+
+def load_emov_db(path_to_EmoV_DB):
+    transcript = os.path.join(path_to_EmoV_DB, 'cmuarctic.data')
+    lines = codecs.open(transcript, 'r', 'utf-8').readlines()
+
+    # in our database, we use only files beginning with arctic_a. And the number of these sentences correspond.
+    # Here we build a dataframe with number and text of each of these lines
+    sentences = []
+    for line in lines:
+        temp = {}
+        idx_n_0 = line.find('arctic_a') + len('arctic_a')
+        if line.find('arctic_a') != -1:
+            print(line)
+            print(idx_n_0)
+            idx_n_end = idx_n_0 + 4
+            number = line[idx_n_0:idx_n_end]
+            print(number)
+            temp['n'] = number
+            idx_text_0 = idx_n_end + 2
+            text = line.strip()[idx_text_0:-3]
+            temp['text'] = text
+            # print(text)
+            sentences.append(temp)
+    sentences = pd.DataFrame(sentences)
+
+    print(sentences)
+    speakers=next(os.walk(path_to_EmoV_DB))[1] #this list directories (and not files, contrary to osl.listdir() )
+
+    data=[]
+
+    for spk in speakers:
+
+        emo_cat = next(os.walk(os.path.join(path_to_EmoV_DB,spk)))[1] #this list directories (and not files, contrary to osl.listdir() )
+
+        for emo in emo_cat:
+            for file in os.listdir(os.path.join(path_to_EmoV_DB, spk, emo)):
+                print(file)
+                fpath = os.path.join(path_to_EmoV_DB, spk, emo, file)
+
+                if file[-4:] == '.wav':
+                    fnumber = file[-8:-4]
+                    print(fnumber)
+                    if fnumber.isdigit():
+                        text = sentences[sentences['n'] == fnumber]['text'].iloc[0]  # result must be a string and not a df with a single element
+                        # text_lengths.append(len(text))
+                        # texts.append(text)
+                        # texts.append(np.array(text, np.int32).tostring())
+                        # fpaths.append(fpath)
+                        # emo_cats.append(emo)
+
+                        e = {'database': 'EmoV-DB',
+                             'id': file[:-4],
+                             'speaker': spk,
+                             'emotion':emo,
+                             'transcription': text,
+                             'sentence_path': fpath}
+                        data.append(e)
+                        print(e)
+
+    data = pd.DataFrame.from_records(data)
+
+    return data
+
+
+def align_db(data):
+    import pathlib
+
+    for i, row in data.iterrows():
+        f = row.sentence_path
+        transcript = row.transcription
+        with gentle.resampled(f) as wavfile:
+            aligner = gentle.ForcedAligner(resources, transcript)
+            result = aligner.transcribe(wavfile, progress_cb=on_progress, logging=logging)
+        # os.system('python align.py '+f+' words.txt -o test.json')
+
+        output = os.path.join('alignments', '/'.join(f.split('/')[-4:]).split('.')[0] + '.json')
+        pathlib.Path('/'.join(output.split('/')[0:-1])).mkdir(parents=True, exist_ok=True)
+
+        fh = open(output, 'w')
+        fh.write(result.to_json(indent=2))
+        if output:
+            logging.info("output written to %s" % (output))
+
+        fh.close()
+
+
+data=load_emov_db(path)
+align_db(data)
+
+
+def get_start_end_from_json(path):
+    a=pd.read_json(os.path.join('file://localhost', os.path.abspath(path)))
+    b=pd.DataFrame.from_records(a.words)
+
+    print('start:')
+    start=b.start[0]
+    print(start)
+
+    print('end:')
+    end=b.end.round(2).tolist()[-1]
+    print(end)
+
+    return start, end
+
+
+# path='alignments/EmoV-DB/bea/amused/amused_1-15_0001.json'
+# start, end=get_start_end_from_json(path)
+
+def play_start_end(path, start, end):
+    import sounddevice as sd
+
+    import librosa
+
+    y,fs=librosa.load(path)
+    sd.play(y[int(start*fs):int(end*fs)],fs)
+
+def play(path):
+    import sounddevice as sd
+
+    import librosa
+
+    y,fs=librosa.load(path)
+    sd.play(y,fs)

emov_mfa_alignment.py

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+import os
+import shutil
+import requests
+import tarfile
+
+import textgrid
+import pandas as pd
+import librosa
+import numpy as np
+from scipy.io import wavfile
+
+class Emov:
+    def __init(self):
+        pass
+
+    def get_all_phone_with_timings(self, f='/home/weili/data/EMOV/1/amused_1-15_0001.TextGrid'):
+        """get all phonemes of a sentence located in tg[1], and filter silence and empty parts, then convert to DataFrame
+        """
+        tg = textgrid.TextGrid.fromFile(f)
+        # get phones and drop "sp", "sil" and empty strings
+        phones=[[el.minTime, el.maxTime, el.mark] for el in tg[1] if el.mark not in ['sil','sp','','spn']]
+        phones=pd.DataFrame(phones)
+        phones.columns=["start", "end", "phone"]
+        return phones
+
+    def convert(self):
+        for speaker in range(1, 5):
+            speaker_path = os.path.join("EMOV-DB", str(speaker))
+            for audio in os.listdir(speaker_path):
+                if audio[-4:] == ".wav":
+                    audio_path = os.path.join(speaker_path, audio)
+                    y, sr = librosa.load(audio_path)
+                    textgrid_path = audio_path.replace("EMOV-DB", "EMOV").replace(".wav", ".TextGrid")
+                    if os.path.exists(textgrid_path):
+                        p = self.get_all_phone_with_timings(f=textgrid_path)  
+                    else:
+                        # wavfile and textfile mismatch
+                        continue
+
+                    speech_segs = np.array([])
+
+                    for interval in p.values:
+                        speech_seg = y[int(interval[0]*sr): int(interval[1]*sr)]
+                        speech_segs = np.append(speech_segs, speech_seg)
+
+                    wavfile.write(textgrid_path.replace(".TextGrid", ".wav"), sr, speech_segs)
+                    
+    
+    def prepare_mfa(self, clean=False):
+        def remove_punct(string): 
+            punctuations = '''!()-[]{};:'"\,<>./?@#$%^&*_~'''
+            for x in string.lower(): 
+                if x in punctuations: 
+                    string = string.replace(x, " ") 
+                                                
+            return string.lower()
+        # create the textfile with the same name of wavfile
+
+        # 1. read transcripts
+        with open("EMOV-DB/cmuarctic.data", "r") as rf:
+            lines = rf.readlines()
+
+        label_to_transcript = {}
+
+        for line in lines:
+            line = line.split('"')
+            sent = line[1]
+            label = line[0].rstrip().split('_')[-1]
+            if label[0] == "b":
+                continue
+            label = label[1:]
+            sent = remove_punct(sent) # remove punct
+            sent = sent.replace("1908", "nineteen o eight")
+            sent = sent.replace("18", "eighteen")
+            sent = sent.replace("16", "sixteen")
+            sent = sent.replace("nightglow", "night glow")
+            sent = sent.replace("mr ", "mister ")
+            sent = sent.replace("mrs ", "misses ")
+            sent = sent.replace("  ", " ")
+            label_to_transcript[label] = sent
+
+        # 2. scan wavfiles and create textfiles
+        for speaker in range(1, 5):
+            speaker_path = os.path.join("EMOV-DB", str(speaker))
+            # for emotion in os.listdir(speaker_path):
+            #     emotion_path = os.path.join(speaker_path, emotion)
+            for audio in os.listdir(speaker_path):
+                if audio[-4:] == ".wav":
+                    textfile = audio[:-4] + ".lab"
+                    label = audio.split('_')[-1].split('.')[0]
+                    transcript = label_to_transcript[label]
+                    if clean:
+                        os.remove(os.path.join(speaker_path, textfile))
+                    else:
+                        with open(os.path.join(speaker_path, textfile), 'w') as wf:
+                            wf.write(transcript)
+                    
+
+    def download(self):
+        download_links = [
+            "https://www.openslr.org/resources/115/bea_Amused.tar.gz",
+            "https://www.openslr.org/resources/115/bea_Angry.tar.gz",
+            "https://www.openslr.org/resources/115/bea_Disgusted.tar.gz",
+            "https://www.openslr.org/resources/115/bea_Neutral.tar.gz",
+            "https://www.openslr.org/resources/115/bea_Sleepy.tar.gz",
+
+            "https://www.openslr.org/resources/115/jenie_Amused.tar.gz",
+            "https://www.openslr.org/resources/115/jenie_Angry.tar.gz",
+            "https://www.openslr.org/resources/115/jenie_Disgusted.tar.gz",
+            "https://www.openslr.org/resources/115/jenie_Neutral.tar.gz",
+            "https://www.openslr.org/resources/115/jenie_Sleepy.tar.gz",
+
+            "https://www.openslr.org/resources/115/josh_Amused.tar.gz",
+            "https://www.openslr.org/resources/115/josh_Neutral.tar.gz",
+            "https://www.openslr.org/resources/115/josh_Sleepy.tar.gz",
+
+            "https://www.openslr.org/resources/115/sam_Amused.tar.gz",
+            "https://www.openslr.org/resources/115/sam_Angry.tar.gz",
+            "https://www.openslr.org/resources/115/sam_Disgusted.tar.gz",
+            "https://www.openslr.org/resources/115/sam_Neutral.tar.gz",
+            "https://www.openslr.org/resources/115/sam_Sleepy.tar.gz",
+
+            "http://www.festvox.org/cmu_arctic/cmuarctic.data"
+        ]
+
+        target_directories = [
+
+            "EMOV-DB/1",
+            "EMOV-DB/1",
+            "EMOV-DB/1",
+            "EMOV-DB/1",
+            "EMOV-DB/1",
+
+            "EMOV-DB/2",
+            "EMOV-DB/2",
+            "EMOV-DB/2",
+            "EMOV-DB/2",
+            "EMOV-DB/2",
+
+            "EMOV-DB/3",
+            "EMOV-DB/3",
+            "EMOV-DB/3",
+
+            "EMOV-DB/4",
+            "EMOV-DB/4",
+            "EMOV-DB/4",
+            "EMOV-DB/4",
+            "EMOV-DB/4",
+
+            "EMOV-DB"
+        ]
+
+        for directory in target_directories:
+            os.makedirs(directory, exist_ok=True)
+
+        for link, target_directory in zip(download_links, target_directories):
+            filename = os.path.basename(link)
+            file_path = os.path.join(target_directory, filename)
+
+            response = requests.get(link, stream=True)
+            if response.status_code == 200:
+                with open(file_path, 'wb') as file:
+                    for chunk in response.iter_content(1024):
+                        file.write(chunk)
+                print(f"download successed:{filename}")
+
+                if filename[-5:]!=".data":
+                    with tarfile.open(file_path, 'r:gz') as tar:
+                        tar.extractall(path=target_directory)
+                    os.remove(file_path)
+            else:
+                print(f"download failed:{filename}")