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Zero
| #%cd SoniTranslate | |
| import numpy as np | |
| import gradio as gr | |
| import whisperx | |
| import torch | |
| from gtts import gTTS | |
| import librosa | |
| import edge_tts | |
| import asyncio | |
| import gc | |
| from pydub import AudioSegment | |
| from tqdm import tqdm | |
| from deep_translator import GoogleTranslator | |
| import os | |
| from soni_translate.audio_segments import create_translated_audio | |
| from soni_translate.text_to_speech import make_voice_gradio | |
| from soni_translate.translate_segments import translate_text | |
| title = "<center><strong><font size='7'>📽️ SoniTranslate 🈷️</font></strong></center>" | |
| news = """ ## 📖 News | |
| 🔥 2023/07/26: new UI and mix options add. | |
| """ | |
| description = """ | |
| ### 🎥 **Translate videos easily with SoniTranslate!** 📽️ | |
| Upload a video or provide a video link. Limitation: 10 seconds for CPU, but no restrictions with a GPU. | |
| For faster results and no duration limits, try the Colab notebook with a GPU: | |
| [](https://github.com/R3gm/SoniTranslate/blob/main/SoniTranslate_Colab.ipynb) | |
| 📽️ **This a demo of SoniTranslate; GitHub repository: [SoniTranslate](https://github.com/R3gm/SoniTranslate)!** | |
| See the tab labeled 'Help' for instructions on how to use it. Let's start having fun with video translation! 🚀🎉 | |
| """ | |
| tutorial = """ | |
| ## 🔰 **Instructions for use:** | |
| 1. 📤 **Upload a video** on the first tab or 🌐 **use a video link** on the second tab. | |
| 2. 🌍 Choose the language in which you want to **translate the video**. | |
| 3. 🗣️ Specify the **number of people speaking** in the video and **assign each one a text-to-speech voice** suitable for the translation language. | |
| 4. 🚀 Press the '**Translate**' button to obtain the results. | |
| """ | |
| # Check GPU | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| list_compute_type = ['float16', 'float32'] | |
| compute_type_default = 'float16' | |
| whisper_model_default = 'large-v1' | |
| else: | |
| device = "cpu" | |
| list_compute_type = ['float32'] | |
| compute_type_default = 'float32' | |
| whisper_model_default = 'base' | |
| print('Working in: ', device) | |
| list_tts = ['af-ZA-AdriNeural-Female', 'af-ZA-WillemNeural-Male', 'am-ET-AmehaNeural-Male', 'am-ET-MekdesNeural-Female', 'ar-AE-FatimaNeural-Female', 'ar-AE-HamdanNeural-Male', 'ar-BH-AliNeural-Male', 'ar-BH-LailaNeural-Female', 'ar-DZ-AminaNeural-Female', 'ar-DZ-IsmaelNeural-Male', 'ar-EG-SalmaNeural-Female', 'ar-EG-ShakirNeural-Male', 'ar-IQ-BasselNeural-Male', 'ar-IQ-RanaNeural-Female', 'ar-JO-SanaNeural-Female', 'ar-JO-TaimNeural-Male', 'ar-KW-FahedNeural-Male', 'ar-KW-NouraNeural-Female', 'ar-LB-LaylaNeural-Female', 'ar-LB-RamiNeural-Male', 'ar-LY-ImanNeural-Female', 'ar-LY-OmarNeural-Male', 'ar-MA-JamalNeural-Male', 'ar-MA-MounaNeural-Female', 'ar-OM-AbdullahNeural-Male', 'ar-OM-AyshaNeural-Female', 'ar-QA-AmalNeural-Female', 'ar-QA-MoazNeural-Male', 'ar-SA-HamedNeural-Male', 'ar-SA-ZariyahNeural-Female', 'ar-SY-AmanyNeural-Female', 'ar-SY-LaithNeural-Male', 'ar-TN-HediNeural-Male', 'ar-TN-ReemNeural-Female', 'ar-YE-MaryamNeural-Female', 'ar-YE-SalehNeural-Male', 'az-AZ-BabekNeural-Male', 'az-AZ-BanuNeural-Female', 'bg-BG-BorislavNeural-Male', 'bg-BG-KalinaNeural-Female', 'bn-BD-NabanitaNeural-Female', 'bn-BD-PradeepNeural-Male', 'bn-IN-BashkarNeural-Male', 'bn-IN-TanishaaNeural-Female', 'bs-BA-GoranNeural-Male', 'bs-BA-VesnaNeural-Female', 'ca-ES-EnricNeural-Male', 'ca-ES-JoanaNeural-Female', 'cs-CZ-AntoninNeural-Male', 'cs-CZ-VlastaNeural-Female', 'cy-GB-AledNeural-Male', 'cy-GB-NiaNeural-Female', 'da-DK-ChristelNeural-Female', 'da-DK-JeppeNeural-Male', 'de-AT-IngridNeural-Female', 'de-AT-JonasNeural-Male', 'de-CH-JanNeural-Male', 'de-CH-LeniNeural-Female', 'de-DE-AmalaNeural-Female', 'de-DE-ConradNeural-Male', 'de-DE-KatjaNeural-Female', 'de-DE-KillianNeural-Male', 'el-GR-AthinaNeural-Female', 'el-GR-NestorasNeural-Male', 'en-AU-NatashaNeural-Female', 'en-AU-WilliamNeural-Male', 'en-CA-ClaraNeural-Female', 'en-CA-LiamNeural-Male', 'en-GB-LibbyNeural-Female', 'en-GB-MaisieNeural-Female', 'en-GB-RyanNeural-Male', 'en-GB-SoniaNeural-Female', 'en-GB-ThomasNeural-Male', 'en-HK-SamNeural-Male', 'en-HK-YanNeural-Female', 'en-IE-ConnorNeural-Male', 'en-IE-EmilyNeural-Female', 'en-IN-NeerjaExpressiveNeural-Female', 'en-IN-NeerjaNeural-Female', 'en-IN-PrabhatNeural-Male', 'en-KE-AsiliaNeural-Female', 'en-KE-ChilembaNeural-Male', 'en-NG-AbeoNeural-Male', 'en-NG-EzinneNeural-Female', 'en-NZ-MitchellNeural-Male', 'en-NZ-MollyNeural-Female', 'en-PH-JamesNeural-Male', 'en-PH-RosaNeural-Female', 'en-SG-LunaNeural-Female', 'en-SG-WayneNeural-Male', 'en-TZ-ElimuNeural-Male', 'en-TZ-ImaniNeural-Female', 'en-US-AnaNeural-Female', 'en-US-AriaNeural-Female', 'en-US-ChristopherNeural-Male', 'en-US-EricNeural-Male', 'en-US-GuyNeural-Male', 'en-US-JennyNeural-Female', 'en-US-MichelleNeural-Female', 'en-US-RogerNeural-Male', 'en-US-SteffanNeural-Male', 'en-ZA-LeahNeural-Female', 'en-ZA-LukeNeural-Male', 'es-AR-ElenaNeural-Female', 'es-AR-TomasNeural-Male', 'es-BO-MarceloNeural-Male', 'es-BO-SofiaNeural-Female', 'es-CL-CatalinaNeural-Female', 'es-CL-LorenzoNeural-Male', 'es-CO-GonzaloNeural-Male', 'es-CO-SalomeNeural-Female', 'es-CR-JuanNeural-Male', 'es-CR-MariaNeural-Female', 'es-CU-BelkysNeural-Female', 'es-CU-ManuelNeural-Male', 'es-DO-EmilioNeural-Male', 'es-DO-RamonaNeural-Female', 'es-EC-AndreaNeural-Female', 'es-EC-LuisNeural-Male', 'es-ES-AlvaroNeural-Male', 'es-ES-ElviraNeural-Female', 'es-GQ-JavierNeural-Male', 'es-GQ-TeresaNeural-Female', 'es-GT-AndresNeural-Male', 'es-GT-MartaNeural-Female', 'es-HN-CarlosNeural-Male', 'es-HN-KarlaNeural-Female', 'es-MX-DaliaNeural-Female', 'es-MX-JorgeNeural-Male', 'es-NI-FedericoNeural-Male', 'es-NI-YolandaNeural-Female', 'es-PA-MargaritaNeural-Female', 'es-PA-RobertoNeural-Male', 'es-PE-AlexNeural-Male', 'es-PE-CamilaNeural-Female', 'es-PR-KarinaNeural-Female', 'es-PR-VictorNeural-Male', 'es-PY-MarioNeural-Male', 'es-PY-TaniaNeural-Female', 'es-SV-LorenaNeural-Female', 'es-SV-RodrigoNeural-Male', 'es-US-AlonsoNeural-Male', 'es-US-PalomaNeural-Female', 'es-UY-MateoNeural-Male', 'es-UY-ValentinaNeural-Female', 'es-VE-PaolaNeural-Female', 'es-VE-SebastianNeural-Male', 'et-EE-AnuNeural-Female', 'et-EE-KertNeural-Male', 'fa-IR-DilaraNeural-Female', 'fa-IR-FaridNeural-Male', 'fi-FI-HarriNeural-Male', 'fi-FI-NooraNeural-Female', 'fil-PH-AngeloNeural-Male', 'fil-PH-BlessicaNeural-Female', 'fr-BE-CharlineNeural-Female', 'fr-BE-GerardNeural-Male', 'fr-CA-AntoineNeural-Male', 'fr-CA-JeanNeural-Male', 'fr-CA-SylvieNeural-Female', 'fr-CH-ArianeNeural-Female', 'fr-CH-FabriceNeural-Male', 'fr-FR-DeniseNeural-Female', 'fr-FR-EloiseNeural-Female', 'fr-FR-HenriNeural-Male', 'ga-IE-ColmNeural-Male', 'ga-IE-OrlaNeural-Female', 'gl-ES-RoiNeural-Male', 'gl-ES-SabelaNeural-Female', 'gu-IN-DhwaniNeural-Female', 'gu-IN-NiranjanNeural-Male', 'he-IL-AvriNeural-Male', 'he-IL-HilaNeural-Female', 'hi-IN-MadhurNeural-Male', 'hi-IN-SwaraNeural-Female', 'hr-HR-GabrijelaNeural-Female', 'hr-HR-SreckoNeural-Male', 'hu-HU-NoemiNeural-Female', 'hu-HU-TamasNeural-Male', 'id-ID-ArdiNeural-Male', 'id-ID-GadisNeural-Female', 'is-IS-GudrunNeural-Female', 'is-IS-GunnarNeural-Male', 'it-IT-DiegoNeural-Male', 'it-IT-ElsaNeural-Female', 'it-IT-IsabellaNeural-Female', 'ja-JP-KeitaNeural-Male', 'ja-JP-NanamiNeural-Female', 'jv-ID-DimasNeural-Male', 'jv-ID-SitiNeural-Female', 'ka-GE-EkaNeural-Female', 'ka-GE-GiorgiNeural-Male', 'kk-KZ-AigulNeural-Female', 'kk-KZ-DauletNeural-Male', 'km-KH-PisethNeural-Male', 'km-KH-SreymomNeural-Female', 'kn-IN-GaganNeural-Male', 'kn-IN-SapnaNeural-Female', 'ko-KR-InJoonNeural-Male', 'ko-KR-SunHiNeural-Female', 'lo-LA-ChanthavongNeural-Male', 'lo-LA-KeomanyNeural-Female', 'lt-LT-LeonasNeural-Male', 'lt-LT-OnaNeural-Female', 'lv-LV-EveritaNeural-Female', 'lv-LV-NilsNeural-Male', 'mk-MK-AleksandarNeural-Male', 'mk-MK-MarijaNeural-Female', 'ml-IN-MidhunNeural-Male', 'ml-IN-SobhanaNeural-Female', 'mn-MN-BataaNeural-Male', 'mn-MN-YesuiNeural-Female', 'mr-IN-AarohiNeural-Female', 'mr-IN-ManoharNeural-Male', 'ms-MY-OsmanNeural-Male', 'ms-MY-YasminNeural-Female', 'mt-MT-GraceNeural-Female', 'mt-MT-JosephNeural-Male', 'my-MM-NilarNeural-Female', 'my-MM-ThihaNeural-Male', 'nb-NO-FinnNeural-Male', 'nb-NO-PernilleNeural-Female', 'ne-NP-HemkalaNeural-Female', 'ne-NP-SagarNeural-Male', 'nl-BE-ArnaudNeural-Male', 'nl-BE-DenaNeural-Female', 'nl-NL-ColetteNeural-Female', 'nl-NL-FennaNeural-Female', 'nl-NL-MaartenNeural-Male', 'pl-PL-MarekNeural-Male', 'pl-PL-ZofiaNeural-Female', 'ps-AF-GulNawazNeural-Male', 'ps-AF-LatifaNeural-Female', 'pt-BR-AntonioNeural-Male', 'pt-BR-FranciscaNeural-Female', 'pt-PT-DuarteNeural-Male', 'pt-PT-RaquelNeural-Female', 'ro-RO-AlinaNeural-Female', 'ro-RO-EmilNeural-Male', 'ru-RU-DmitryNeural-Male', 'ru-RU-SvetlanaNeural-Female', 'si-LK-SameeraNeural-Male', 'si-LK-ThiliniNeural-Female', 'sk-SK-LukasNeural-Male', 'sk-SK-ViktoriaNeural-Female', 'sl-SI-PetraNeural-Female', 'sl-SI-RokNeural-Male', 'so-SO-MuuseNeural-Male', 'so-SO-UbaxNeural-Female', 'sq-AL-AnilaNeural-Female', 'sq-AL-IlirNeural-Male', 'sr-RS-NicholasNeural-Male', 'sr-RS-SophieNeural-Female', 'su-ID-JajangNeural-Male', 'su-ID-TutiNeural-Female', 'sv-SE-MattiasNeural-Male', 'sv-SE-SofieNeural-Female', 'sw-KE-RafikiNeural-Male', 'sw-KE-ZuriNeural-Female', 'sw-TZ-DaudiNeural-Male', 'sw-TZ-RehemaNeural-Female', 'ta-IN-PallaviNeural-Female', 'ta-IN-ValluvarNeural-Male', 'ta-LK-KumarNeural-Male', 'ta-LK-SaranyaNeural-Female', 'ta-MY-KaniNeural-Female', 'ta-MY-SuryaNeural-Male', 'ta-SG-AnbuNeural-Male', 'ta-SG-VenbaNeural-Female', 'te-IN-MohanNeural-Male', 'te-IN-ShrutiNeural-Female', 'th-TH-NiwatNeural-Male', 'th-TH-PremwadeeNeural-Female', 'tr-TR-AhmetNeural-Male', 'tr-TR-EmelNeural-Female', 'uk-UA-OstapNeural-Male', 'uk-UA-PolinaNeural-Female', 'ur-IN-GulNeural-Female', 'ur-IN-SalmanNeural-Male', 'ur-PK-AsadNeural-Male', 'ur-PK-UzmaNeural-Female', 'uz-UZ-MadinaNeural-Female', 'uz-UZ-SardorNeural-Male', 'vi-VN-HoaiMyNeural-Female', 'vi-VN-NamMinhNeural-Male', 'zh-CN-XiaoxiaoNeural-Female', 'zh-CN-XiaoyiNeural-Female', 'zh-CN-YunjianNeural-Male', 'zh-CN-YunxiNeural-Male', 'zh-CN-YunxiaNeural-Male', 'zh-CN-YunyangNeural-Male', 'zh-CN-liaoning-XiaobeiNeural-Female', 'zh-CN-shaanxi-XiaoniNeural-Female'] | |
| ''' | |
| def translate_from_video(video, WHISPER_MODEL_SIZE, batch_size, compute_type, | |
| TRANSLATE_AUDIO_TO, min_speakers, max_speakers, | |
| tts_voice00, tts_voice01,tts_voice02,tts_voice03,tts_voice04,tts_voice05): | |
| YOUR_HF_TOKEN = os.getenv("My_hf_token") | |
| create_translated_audio(result_diarize, audio_files, Output_name_file) | |
| os.system("rm audio_dub_stereo.wav") | |
| os.system("ffmpeg -i audio_dub_solo.wav -ac 1 audio_dub_stereo.wav") | |
| os.system(f"rm {mix_audio}") | |
| os.system(f'ffmpeg -y -i audio.wav -i audio_dub_stereo.wav -filter_complex "[0:0]volume=0.15[a];[1:0]volume=1.90[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio}') | |
| os.system(f"rm {video_output}") | |
| os.system(f"ffmpeg -i {OutputFile} -i {mix_audio} -c:v copy -c:a copy -map 0:v -map 1:a -shortest {video_output}") | |
| return video_output | |
| ''' | |
| def translate_from_video( | |
| video, | |
| YOUR_HF_TOKEN, | |
| preview=False, | |
| WHISPER_MODEL_SIZE="large-v1", | |
| batch_size=16, | |
| compute_type="float16", | |
| SOURCE_LANGUAGE= "Automatic detection", | |
| TRANSLATE_AUDIO_TO="English (en)", | |
| min_speakers=1, | |
| max_speakers=2, | |
| tts_voice00="en-AU-WilliamNeural-Male", | |
| tts_voice01="en-CA-ClaraNeural-Female", | |
| tts_voice02="en-GB-ThomasNeural-Male", | |
| tts_voice03="en-GB-SoniaNeural-Female", | |
| tts_voice04="en-NZ-MitchellNeural-Male", | |
| tts_voice05="en-GB-MaisieNeural-Female", | |
| video_output="video_dub.mp4", | |
| AUDIO_MIX_METHOD='Adjusting volumes and mixing audio', | |
| ): | |
| if YOUR_HF_TOKEN == "" or YOUR_HF_TOKEN == None: | |
| YOUR_HF_TOKEN = os.getenv("YOUR_HF_TOKEN") | |
| if YOUR_HF_TOKEN == None: | |
| print('No valid token') | |
| return | |
| if "SET_LIMIT" == os.getenv("DEMO"): | |
| preview=True | |
| print("DEMO; set preview=True; The generation is **limited to 10 seconds** to prevent errors with the CPU. If you use a GPU, you won't have any of these limitations.") | |
| AUDIO_MIX_METHOD='Adjusting volumes and mixing audio' | |
| print("DEMO; set Adjusting volumes and mixing audio") | |
| LANGUAGES = { | |
| 'Automatic detection': 'Automatic detection', | |
| 'English (en)': 'en', | |
| 'French (fr)': 'fr', | |
| 'German (de)': 'de', | |
| 'Spanish (es)': 'es', | |
| 'Italian (it)': 'it', | |
| 'Japanese (ja)': 'ja', | |
| 'Chinese (zh)': 'zh', | |
| 'Dutch (nl)': 'nl', | |
| 'Ukrainian (uk)': 'uk', | |
| 'Portuguese (pt)': 'pt' | |
| } | |
| TRANSLATE_AUDIO_TO = LANGUAGES[TRANSLATE_AUDIO_TO] | |
| SOURCE_LANGUAGE = LANGUAGES[SOURCE_LANGUAGE] | |
| if not os.path.exists('audio'): | |
| os.makedirs('audio') | |
| if not os.path.exists('audio2/audio'): | |
| os.makedirs('audio2/audio') | |
| # Check GPU | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| compute_type = "float32" if device == "cpu" else compute_type | |
| OutputFile = 'Video.mp4' | |
| audio_wav = "audio.wav" | |
| Output_name_file = "audio_dub_solo.ogg" | |
| mix_audio = "audio_mix.mp3" | |
| os.system("rm Video.mp4") | |
| os.system("rm audio.webm") | |
| os.system("rm audio.wav") | |
| if os.path.exists(video): | |
| if preview: | |
| print('Creating a preview video of 10 seconds, to disable this option, go to advanced settings and turn off preview.') | |
| os.system(f'ffmpeg -y -i "{video}" -ss 00:00:20 -t 00:00:10 -c:v libx264 -c:a aac -strict experimental Video.mp4') | |
| else: | |
| os.system(f'ffmpeg -y -i "{video}" -c:v libx264 -c:a aac -strict experimental Video.mp4') | |
| os.system("ffmpeg -y -i Video.mp4 -vn -acodec pcm_s16le -ar 44100 -ac 2 audio.wav") | |
| else: | |
| if preview: | |
| print('Creating a preview from the link, 10 seconds to disable this option, go to advanced settings and turn off preview.') | |
| #https://github.com/yt-dlp/yt-dlp/issues/2220 | |
| mp4_ = f'yt-dlp -f "mp4" --downloader ffmpeg --downloader-args "ffmpeg_i: -ss 00:00:20 -t 00:00:10" --force-overwrites --max-downloads 1 --no-warnings --no-abort-on-error --ignore-no-formats-error --restrict-filenames -o {OutputFile} {video}' | |
| wav_ = "ffmpeg -y -i Video.mp4 -vn -acodec pcm_s16le -ar 44100 -ac 2 audio.wav" | |
| os.system(mp4_) | |
| os.system(wav_) | |
| else: | |
| mp4_ = f'yt-dlp -f "mp4" --force-overwrites --max-downloads 1 --no-warnings --no-abort-on-error --ignore-no-formats-error --restrict-filenames -o {OutputFile} {video}' | |
| wav_ = f'python -m yt_dlp --output {audio_wav} --force-overwrites --max-downloads 1 --no-warnings --no-abort-on-error --ignore-no-formats-error --extract-audio --audio-format wav {video}' | |
| os.system(wav_) | |
| for i in range (120): | |
| time.sleep(1) | |
| print('process audio...') | |
| if os.path.exists(audio_wav) and not os.path.exists('audio.webm'): | |
| time.sleep(1) | |
| os.system(mp4_) | |
| break | |
| if i == 119: | |
| print('Error donwloading the audio') | |
| return | |
| print("Set file complete.") | |
| SOURCE_LANGUAGE = None if SOURCE_LANGUAGE == 'Automatic detection' else SOURCE_LANGUAGE | |
| # 1. Transcribe with original whisper (batched) | |
| model = whisperx.load_model( | |
| WHISPER_MODEL_SIZE, | |
| device, | |
| compute_type=compute_type, | |
| language= SOURCE_LANGUAGE, | |
| ) | |
| audio = whisperx.load_audio(audio_wav) | |
| result = model.transcribe(audio, batch_size=batch_size) | |
| gc.collect(); torch.cuda.empty_cache(); del model | |
| print("Transcript complete") | |
| # 2. Align whisper output | |
| model_a, metadata = whisperx.load_align_model( | |
| language_code=result["language"], | |
| device=device | |
| ) | |
| result = whisperx.align( | |
| result["segments"], | |
| model_a, | |
| metadata, | |
| audio, | |
| device, | |
| return_char_alignments=True, | |
| ) | |
| gc.collect(); torch.cuda.empty_cache(); del model_a | |
| print("Align complete") | |
| if result['segments'] == []: | |
| print('No active speech found in audio') | |
| return | |
| # 3. Assign speaker labels | |
| diarize_model = whisperx.DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device) | |
| diarize_segments = diarize_model( | |
| audio_wav, | |
| min_speakers=min_speakers, | |
| max_speakers=max_speakers) | |
| result_diarize = whisperx.assign_word_speakers(diarize_segments, result) | |
| gc.collect(); torch.cuda.empty_cache(); del diarize_model | |
| print("Diarize complete") | |
| result_diarize['segments'] = translate_text(result_diarize['segments'], TRANSLATE_AUDIO_TO) | |
| print("Translation complete") | |
| audio_files = [] | |
| # Mapping speakers to voice variables | |
| speaker_to_voice = { | |
| 'SPEAKER_00': tts_voice00, | |
| 'SPEAKER_01': tts_voice01, | |
| 'SPEAKER_02': tts_voice02, | |
| 'SPEAKER_03': tts_voice03, | |
| 'SPEAKER_04': tts_voice04, | |
| 'SPEAKER_05': tts_voice05 | |
| } | |
| for segment in tqdm(result_diarize['segments']): | |
| text = segment['text'] | |
| start = segment['start'] | |
| end = segment['end'] | |
| try: | |
| speaker = segment['speaker'] | |
| except KeyError: | |
| segment['speaker'] = "SPEAKER_99" | |
| speaker = segment['speaker'] | |
| print("NO SPEAKER DETECT IN SEGMENT") | |
| # make the tts audio | |
| filename = f"audio/{start}.ogg" | |
| if speaker in speaker_to_voice and speaker_to_voice[speaker] != 'None': | |
| make_voice_gradio(text, speaker_to_voice[speaker], filename, TRANSLATE_AUDIO_TO) | |
| elif speaker == "SPEAKER_99": | |
| try: | |
| tts = gTTS(text, lang=TRANSLATE_AUDIO_TO) | |
| tts.save(filename) | |
| print('Using GTTS') | |
| except: | |
| tts = gTTS('a', lang=TRANSLATE_AUDIO_TO) | |
| tts.save(filename) | |
| print('Error: Audio will be replaced.') | |
| # duration | |
| duration_true = end - start | |
| duration_tts = librosa.get_duration(filename=filename) | |
| # porcentaje | |
| porcentaje = duration_tts / duration_true | |
| if porcentaje > 2.1: | |
| porcentaje = 2.1 | |
| elif porcentaje <= 1.2 and porcentaje >= 0.8: | |
| porcentaje = 1.0 | |
| elif porcentaje <= 0.79: | |
| porcentaje = 0.8 | |
| # Smoth and round | |
| porcentaje = round(porcentaje+0.0, 1) | |
| # apply aceleration or opposite to the audio file in audio2 folder | |
| os.system(f"ffmpeg -y -loglevel panic -i {filename} -filter:a atempo={porcentaje} audio2/{filename}") | |
| duration_create = librosa.get_duration(filename=f"audio2/{filename}") | |
| audio_files.append(filename) | |
| # replace files with the accelerates | |
| os.system("mv -f audio2/audio/*.ogg audio/") | |
| os.system(f"rm {Output_name_file}") | |
| create_translated_audio(result_diarize, audio_files, Output_name_file) | |
| os.system(f"rm {mix_audio}") | |
| # TYPE MIX AUDIO | |
| if AUDIO_MIX_METHOD == 'Adjusting volumes and mixing audio': | |
| # volume mix | |
| os.system(f'ffmpeg -y -i {audio_wav} -i {Output_name_file} -filter_complex "[0:0]volume=0.15[a];[1:0]volume=1.90[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio}') | |
| else: | |
| try: | |
| # background mix | |
| os.system(f'ffmpeg -i {audio_wav} -i {Output_name_file} -filter_complex "[1:a]asplit=2[sc][mix];[0:a][sc]sidechaincompress=threshold=0.003:ratio=20[bg]; [bg][mix]amerge[final]" -map [final] {mix_audio}') | |
| except: | |
| # volume mix except | |
| os.system(f'ffmpeg -y -i {audio_wav} -i {Output_name_file} -filter_complex "[0:0]volume=0.15[a];[1:0]volume=1.90[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio}') | |
| os.system(f"rm {video_output}") | |
| os.system(f"ffmpeg -i {OutputFile} -i {mix_audio} -c:v copy -c:a copy -map 0:v -map 1:a -shortest {video_output}") | |
| return video_output | |
| import sys | |
| class Logger: | |
| def __init__(self, filename): | |
| self.terminal = sys.stdout | |
| self.log = open(filename, "w") | |
| def write(self, message): | |
| self.terminal.write(message) | |
| self.log.write(message) | |
| def flush(self): | |
| self.terminal.flush() | |
| self.log.flush() | |
| def isatty(self): | |
| return False | |
| sys.stdout = Logger("output.log") | |
| def read_logs(): | |
| sys.stdout.flush() | |
| with open("output.log", "r") as f: | |
| return f.read() | |
| # max tts | |
| MAX_TTS = 6 | |
| theme='Taithrah/Minimal' | |
| with gr.Blocks(theme=theme) as demo: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| #### video | |
| with gr.Tab("Translate audio from video"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| video_input = gr.Video() # height=300,width=300 | |
| SOURCE_LANGUAGE = gr.Dropdown(['Automatic detection', 'English (en)', 'French (fr)', 'German (de)', 'Spanish (es)', 'Italian (it)', 'Japanese (ja)', 'Chinese (zh)', 'Dutch (nl)', 'Ukrainian (uk)', 'Portuguese (pt)'], value='Automatic detection',label = 'Source language', info="This is the original language of the video") | |
| TRANSLATE_AUDIO_TO = gr.Dropdown(['English (en)', 'French (fr)', 'German (de)', 'Spanish (es)', 'Italian (it)', 'Japanese (ja)', 'Chinese (zh)', 'Dutch (nl)', 'Ukrainian (uk)', 'Portuguese (pt)'], value='English (en)',label = 'Translate audio to', info="Select the target language, and make sure to select the language corresponding to the speakers of the target language to avoid errors in the process.") | |
| line_ = gr.HTML("<hr></h2>") | |
| gr.Markdown("Select how many people are speaking in the video.") | |
| min_speakers = gr.Slider(1, MAX_TTS, default=1, label="min_speakers", step=1, visible=False) | |
| max_speakers = gr.Slider(1, MAX_TTS, value=2, step=1, label="Max speakers", interative=True) | |
| gr.Markdown("Select the voice you want for each speaker.") | |
| def submit(value): | |
| visibility_dict = { | |
| f'tts_voice{i:02d}': gr.update(visible=i < value) for i in range(6) | |
| } | |
| return [value for value in visibility_dict.values()] | |
| tts_voice00 = gr.Dropdown(list_tts, value='en-AU-WilliamNeural-Male', label = 'TTS Speaker 1', visible=True, interactive= True) | |
| tts_voice01 = gr.Dropdown(list_tts, value='en-CA-ClaraNeural-Female', label = 'TTS Speaker 2', visible=True, interactive= True) | |
| tts_voice02 = gr.Dropdown(list_tts, value='en-GB-ThomasNeural-Male', label = 'TTS Speaker 3', visible=False, interactive= True) | |
| tts_voice03 = gr.Dropdown(list_tts, value='en-GB-SoniaNeural-Female', label = 'TTS Speaker 4', visible=False, interactive= True) | |
| tts_voice04 = gr.Dropdown(list_tts, value='en-NZ-MitchellNeural-Male', label = 'TTS Speaker 5', visible=False, interactive= True) | |
| tts_voice05 = gr.Dropdown(list_tts, value='en-GB-MaisieNeural-Female', label = 'TTS Speaker 6', visible=False, interactive= True) | |
| max_speakers.change(submit, max_speakers, [tts_voice00, tts_voice01, tts_voice02, tts_voice03, tts_voice04, tts_voice05]) | |
| with gr.Column(): | |
| with gr.Accordion("Advanced Settings", open=False): | |
| AUDIO_MIX = gr.Dropdown(['Mixing audio with sidechain compression', 'Adjusting volumes and mixing audio'], value='Adjusting volumes and mixing audio', label = 'Audio Mixing Method', info="Mix original and translated audio files to create a customized, balanced output with two available mixing modes.") | |
| gr.HTML("<hr></h2>") | |
| gr.Markdown("Default configuration of Whisper.") | |
| WHISPER_MODEL_SIZE = gr.inputs.Dropdown(['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2'], default=whisper_model_default, label="Whisper model") | |
| batch_size = gr.inputs.Slider(1, 32, default=16, label="Batch size", step=1) | |
| compute_type = gr.inputs.Dropdown(list_compute_type, default=compute_type_default, label="Compute type") | |
| gr.HTML("<hr></h2>") | |
| VIDEO_OUTPUT_NAME = gr.Textbox(label="Translated file name" ,value="video_output.mp4", info="The name of the output file") | |
| PREVIEW = gr.Checkbox(label="Preview", info="Preview cuts the video to only 10 seconds for testing purposes. Please deactivate it to retrieve the full video duration.") | |
| with gr.Column(variant='compact'): | |
| with gr.Row(): | |
| video_button = gr.Button("TRANSLATE", ) | |
| with gr.Row(): | |
| video_output = gr.Video() | |
| line_ = gr.HTML("<hr></h2>") | |
| if os.getenv("YOUR_HF_TOKEN") == None or os.getenv("YOUR_HF_TOKEN") == "": | |
| HFKEY = gr.Textbox(visible= True, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...") | |
| else: | |
| HFKEY = gr.Textbox(visible= False, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...") | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| "./assets/Video_main.mp4", | |
| "", | |
| True, | |
| "base", | |
| 16, | |
| "float32", | |
| "Spanish (es)", | |
| "English (en)", | |
| 1, | |
| 2, | |
| 'en-AU-WilliamNeural-Male', | |
| 'en-CA-ClaraNeural-Female', | |
| 'en-GB-ThomasNeural-Male', | |
| 'en-GB-SoniaNeural-Female', | |
| 'en-NZ-MitchellNeural-Male', | |
| 'en-GB-MaisieNeural-Female', | |
| "video_output.mp4", | |
| 'Adjusting volumes and mixing audio', | |
| ], | |
| ], | |
| fn=translate_from_video, | |
| inputs=[ | |
| video_input, | |
| HFKEY, | |
| PREVIEW, | |
| WHISPER_MODEL_SIZE, | |
| batch_size, | |
| compute_type, | |
| SOURCE_LANGUAGE, | |
| TRANSLATE_AUDIO_TO, | |
| min_speakers, | |
| max_speakers, | |
| tts_voice00, | |
| tts_voice01, | |
| tts_voice02, | |
| tts_voice03, | |
| tts_voice04, | |
| tts_voice05, | |
| VIDEO_OUTPUT_NAME, | |
| AUDIO_MIX, | |
| ], | |
| outputs=[video_output], | |
| cache_examples=True, | |
| ) | |
| ### link | |
| with gr.Tab("Translate audio from video link"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| blink_input = gr.Textbox(label="Media link.", info="Example: www.youtube.com/watch?v=g_9rPvbENUw", placeholder="URL goes here...") | |
| # bSOURCE_LANGUAGE = gr.Dropdown(['Automatic detection', 'en', 'fr', 'de', 'es', 'it', 'ja', 'zh', 'nl', 'uk', 'pt'], value='en',label = 'Source language') | |
| # gr.HTML("<hr></h2>") | |
| # bHFKEY = gr.Textbox(label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...") | |
| # gr.Markdown("Select the target language, and make sure to select the language corresponding to the speakers of the target language to avoid errors in the process.") | |
| # bTRANSLATE_AUDIO_TO = gr.inputs.Dropdown(['en', 'fr', 'de', 'es', 'it', 'ja', 'zh', 'nl', 'uk', 'pt'], default='en',label = 'Translate audio to') | |
| # gr.Markdown("Select how many people are speaking in the video.") | |
| # bmin_speakers = gr.inputs.Slider(1, 6, default=1, label="min_speakers", step=1, ) | |
| # bmax_speakers = gr.inputs.Slider(1, 6, default=2, label="max_speakers",step=1) | |
| # gr.Markdown("Select the voice you want for each speaker.") | |
| # btts_voice00 = gr.inputs.Dropdown(list_tts, default='en-AU-WilliamNeural-Male', label = 'TTS Speaker 1') | |
| # btts_voice01 = gr.inputs.Dropdown(list_tts, default='en-CA-ClaraNeural-Female', label = 'TTS Speaker 2') | |
| # btts_voice02 = gr.inputs.Dropdown(list_tts, default='en-GB-ThomasNeural-Male', label = 'TTS Speaker 3') | |
| # btts_voice03 = gr.inputs.Dropdown(list_tts, default='en-GB-SoniaNeural-Female', label = 'TTS Speaker 4') | |
| # btts_voice04 = gr.inputs.Dropdown(list_tts, default='en-NZ-MitchellNeural-Male', label = 'TTS Speaker 5') | |
| # btts_voice05 = gr.inputs.Dropdown(list_tts, default='en-GB-MaisieNeural-Female', label = 'TTS Speaker 6') | |
| # with gr.Column(): | |
| # with gr.Accordion("Advanced Settings", open=False): | |
| # gr.Markdown("Default configuration of Whisper.") | |
| # bWHISPER_MODEL_SIZE = gr.inputs.Dropdown(['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2'], default=whisper_model_default, label="Whisper model") | |
| # bbatch_size = gr.inputs.Slider(1, 32, default=16, label="Batch size", step=1) | |
| # bcompute_type = gr.inputs.Dropdown(list_compute_type, default=compute_type_default, label="Compute type") | |
| # bPREVIEW = gr.inputs.Checkbox(label="Preview cuts the video to only 10 seconds for testing purposes. Please deactivate it to retrieve the full video duration.") | |
| # bVIDEO_OUTPUT_NAME = gr.Textbox(label="Translated file name" ,value="video_output.mp4") | |
| bSOURCE_LANGUAGE = gr.Dropdown(['Automatic detection', 'English (en)', 'French (fr)', 'German (de)', 'Spanish (es)', 'Italian (it)', 'Japanese (ja)', 'Chinese (zh)', 'Dutch (nl)', 'Ukrainian (uk)', 'Portuguese (pt)'], value='Automatic detection',label = 'Source language', info="This is the original language of the video") | |
| bTRANSLATE_AUDIO_TO = gr.Dropdown(['English (en)', 'French (fr)', 'German (de)', 'Spanish (es)', 'Italian (it)', 'Japanese (ja)', 'Chinese (zh)', 'Dutch (nl)', 'Ukrainian (uk)', 'Portuguese (pt)'], value='English (en)',label = 'Translate audio to', info="Select the target language, and make sure to select the language corresponding to the speakers of the target language to avoid errors in the process.") | |
| bline_ = gr.HTML("<hr></h2>") | |
| gr.Markdown("Select how many people are speaking in the video.") | |
| bmin_speakers = gr.Slider(1, MAX_TTS, default=1, label="min_speakers", step=1, visible=False) | |
| bmax_speakers = gr.Slider(1, MAX_TTS, value=2, step=1, label="Max speakers", interative=True) | |
| gr.Markdown("Select the voice you want for each speaker.") | |
| def bsubmit(value): | |
| visibility_dict = { | |
| f'btts_voice{i:02d}': gr.update(visible=i < value) for i in range(6) | |
| } | |
| return [value for value in visibility_dict.values()] | |
| btts_voice00 = gr.Dropdown(list_tts, value='en-AU-WilliamNeural-Male', label = 'TTS Speaker 1', visible=True, interactive= True) | |
| btts_voice01 = gr.Dropdown(list_tts, value='en-CA-ClaraNeural-Female', label = 'TTS Speaker 2', visible=True, interactive= True) | |
| btts_voice02 = gr.Dropdown(list_tts, value='en-GB-ThomasNeural-Male', label = 'TTS Speaker 3', visible=False, interactive= True) | |
| btts_voice03 = gr.Dropdown(list_tts, value='en-GB-SoniaNeural-Female', label = 'TTS Speaker 4', visible=False, interactive= True) | |
| btts_voice04 = gr.Dropdown(list_tts, value='en-NZ-MitchellNeural-Male', label = 'TTS Speaker 5', visible=False, interactive= True) | |
| btts_voice05 = gr.Dropdown(list_tts, value='en-GB-MaisieNeural-Female', label = 'TTS Speaker 6', visible=False, interactive= True) | |
| bmax_speakers.change(bsubmit, bmax_speakers, [btts_voice00, btts_voice01, btts_voice02, btts_voice03, btts_voice04, btts_voice05]) | |
| with gr.Column(): | |
| with gr.Accordion("Advanced Settings", open=False): | |
| bAUDIO_MIX = gr.Dropdown(['Mixing audio with sidechain compression', 'Adjusting volumes and mixing audio'], value='Adjusting volumes and mixing audio', label = 'Audio Mixing Method', info="Mix original and translated audio files to create a customized, balanced output with two available mixing modes.") | |
| gr.HTML("<hr></h2>") | |
| gr.Markdown("Default configuration of Whisper.") | |
| bWHISPER_MODEL_SIZE = gr.inputs.Dropdown(['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2'], default=whisper_model_default, label="Whisper model") | |
| bbatch_size = gr.inputs.Slider(1, 32, default=16, label="Batch size", step=1) | |
| bcompute_type = gr.inputs.Dropdown(list_compute_type, default=compute_type_default, label="Compute type") | |
| gr.HTML("<hr></h2>") | |
| bVIDEO_OUTPUT_NAME = gr.Textbox(label="Translated file name" ,value="video_output.mp4", info="The name of the output file") | |
| bPREVIEW = gr.Checkbox(label="Preview", info="Preview cuts the video to only 10 seconds for testing purposes. Please deactivate it to retrieve the full video duration.") | |
| # text_button = gr.Button("Translate audio of video") | |
| # link_output = gr.Video() #gr.outputs.File(label="Download!") | |
| with gr.Column(variant='compact'): | |
| with gr.Row(): | |
| text_button = gr.Button("TRANSLATE") | |
| with gr.Row(): | |
| blink_output = gr.Video() #gr.outputs.File(label="Download!") # gr.Video() | |
| bline_ = gr.HTML("<hr></h2>") | |
| if os.getenv("YOUR_HF_TOKEN") == None or os.getenv("YOUR_HF_TOKEN") == "": | |
| bHFKEY = gr.Textbox(visible= True, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...") | |
| else: | |
| bHFKEY = gr.Textbox(visible= False, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...") | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| "https://www.youtube.com/watch?v=5ZeHtRKHl7Y", | |
| "", | |
| True, | |
| "base", | |
| 16, | |
| "float32", | |
| "Japanese (ja)", | |
| "English (en)", | |
| 1, | |
| 2, | |
| 'en-CA-ClaraNeural-Female', | |
| 'en-AU-WilliamNeural-Male', | |
| 'en-GB-ThomasNeural-Male', | |
| 'en-GB-SoniaNeural-Female', | |
| 'en-NZ-MitchellNeural-Male', | |
| 'en-GB-MaisieNeural-Female', | |
| "video_output.mp4", | |
| 'Adjusting volumes and mixing audio', | |
| ], | |
| ], | |
| fn=translate_from_video, | |
| inputs=[ | |
| blink_input, | |
| bHFKEY, | |
| bPREVIEW, | |
| bWHISPER_MODEL_SIZE, | |
| bbatch_size, | |
| bcompute_type, | |
| bSOURCE_LANGUAGE, | |
| bTRANSLATE_AUDIO_TO, | |
| bmin_speakers, | |
| bmax_speakers, | |
| btts_voice00, | |
| btts_voice01, | |
| btts_voice02, | |
| btts_voice03, | |
| btts_voice04, | |
| btts_voice05, | |
| bVIDEO_OUTPUT_NAME, | |
| bAUDIO_MIX | |
| ], | |
| outputs=[blink_output], | |
| cache_examples=True, | |
| ) | |
| with gr.Tab("Help"): | |
| gr.Markdown(news) | |
| gr.Markdown(tutorial) | |
| with gr.Accordion("Logs", open = False): | |
| logs = gr.Textbox() | |
| demo.load(read_logs, None, logs, every=1) | |
| # run | |
| video_button.click(translate_from_video, inputs=[ | |
| video_input, | |
| HFKEY, | |
| PREVIEW, | |
| WHISPER_MODEL_SIZE, | |
| batch_size, | |
| compute_type, | |
| SOURCE_LANGUAGE, | |
| TRANSLATE_AUDIO_TO, | |
| min_speakers, | |
| max_speakers, | |
| tts_voice00, | |
| tts_voice01, | |
| tts_voice02, | |
| tts_voice03, | |
| tts_voice04, | |
| tts_voice05, | |
| VIDEO_OUTPUT_NAME, | |
| AUDIO_MIX, | |
| ], outputs=video_output) | |
| text_button.click(translate_from_video, inputs=[ | |
| blink_input, | |
| bHFKEY, | |
| bPREVIEW, | |
| bWHISPER_MODEL_SIZE, | |
| bbatch_size, | |
| bcompute_type, | |
| bSOURCE_LANGUAGE, | |
| bTRANSLATE_AUDIO_TO, | |
| bmin_speakers, | |
| bmax_speakers, | |
| btts_voice00, | |
| btts_voice01, | |
| btts_voice02, | |
| btts_voice03, | |
| btts_voice04, | |
| btts_voice05, | |
| bVIDEO_OUTPUT_NAME, | |
| bAUDIO_MIX, | |
| ], outputs=blink_output) | |
| demo.launch(enable_queue=True) | |
| #demo.launch() |