add base app

.github/workflows/main.yml

@@ -0,0 +1,23 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Add remote 
+        env:
+          HF: ${{ secrets.HF }}
+        run: git remote add space https://r3gm:[email protected]/spaces/r3gm/SoniTranslate_translate_audio_of_a_video_content
+      - name: Push to hub
+        env:
+          HF: ${{ secrets.HF }}
+        run: git push --force https://r3gm:[email protected]/spaces/r3gm/SoniTranslate_translate_audio_of_a_video_content main

README.md

@@ -1,2 +1,12 @@
-# sonitranslate_app_hf
-hugging face app
+---
+title: SoniTranslate_translate_audio_of_a_video_content
+emoji: 🦀
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: true
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,470 @@
+
+#os.system("git clone https://github.com/R3gm/SoniTranslate")
+# pip install -r requirements.txt
+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
+#from soni_translate import test
+
+title = "<center><strong><font size='7'>📽️ SoniTranslate 🈷️</font></strong></center>"
+
+news = """ ## 📖 News
+        🔥 2023/07/01: Support  (Thanks for [text](https://github.com)).
+        """  
+
+description = """ ## Translate the audio of a video content from one language to another while preserving synchronization.
+
+
+                This is a demo on Github project 📽️ [SoniTranslate](https://github.com/R3gm/SoniTranslate).
+                
+                📼 You can upload a video or provide a video link. The generation is **limited to 10 seconds** to prevent errors with the queue in cpu. If you use a GPU, you won't have any of these limitations.
+                
+                🚀 For **translate a video of any duration** and faster results, you can use the Colab notebook with GPU.   
+
+                [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/R3gm/SoniTranslate/blob/main/SoniTranslate_Colab.ipynb)
+                 
+              """
+
+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.
+                
+              """
+
+
+if not os.path.exists('audio'):
+    os.makedirs('audio')
+
+if not os.path.exists('audio2/audio'):
+    os.makedirs('audio2/audio')
+
+# 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)
+
+
+# Download an audio
+#url = "https://www.youtube.com/watch?v=Rdi-SNhe2v4"
+
+### INIT
+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")
+                             
+    OutputFile = 'Video.mp4'
+    audio_wav = "audio.wav"
+    Output_name_file = "audio_dub_solo.wav"
+    mix_audio = "audio_mix.mp3"
+    video_output = "diar_output.mp4"
+                             
+    os.system(f"rm {Output_name_file}")
+    os.system("rm Video.mp4")
+    #os.system("rm diar_output.mp4")
+    os.system("rm audio.wav")
+                            
+
+    if os.path.exists(video):     
+        if device == 'cpu':
+            # max 1 minute in cpu
+            print('10 s. Limited for CPU ')
+            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 device == 'cpu':
+            # max 1 minute in cpu
+            print('10 s. Limited for CPU ')
+            #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 1 audio.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(mp4_)
+        os.system(wav_)
+
+    print("Set file complete.")
+        
+    # 1. Transcribe with original whisper (batched)
+    model = whisperx.load_model(
+        WHISPER_MODEL_SIZE,
+        device,
+        compute_type=compute_type
+        )
+    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")
+                             
+    # 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 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)
+        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 GTTS')
+
+        # 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("rm audio_dub_stereo.wav")                        
+    os.system("ffmpeg -i audio_dub_solo.wav -ac 1 audio_dub_stereo.wav")
+                             
+    #os.system(f"ffmpeg -i Video.mp4 -i {Output_name_file} -c:v copy -c:a copy -map 0:v -map 1:a -shortest {video_output}")
+
+    os.system(f"rm {mix_audio}")
+    #os.system(f'''ffmpeg -i {audio_wav} -i audio_dub_stereo.wav -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}''')
+    #os.system(f'ffmpeg -y -i {audio_wav} -i audio_dub_stereo.wav -filter_complex "[0:0][1:0] amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio}')
+    os.system(f'ffmpeg -y -i audio.wav -i audio_dub_stereo.wav -filter_complex "[0:0]volume=0.25[a];[1:0]volume=1.85[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()
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    gr.Markdown(tutorial)
+
+    with gr.Tab("Translate audio from video"):
+        with gr.Row():
+            with gr.Column():
+                video_input = gr.Video() # height=300,width=300
+                
+                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.")  
+                TRANSLATE_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.")
+                min_speakers = gr.inputs.Slider(1, 6, default=1, label="min_speakers", step=1)
+                max_speakers = gr.inputs.Slider(1, 6, default=2, label="max_speakers",step=1) 
+                  
+                gr.Markdown("Select the voice you want for each speaker.")
+                tts_voice00 = gr.inputs.Dropdown(list_tts, default='en-AU-WilliamNeural-Male', label = 'TTS Speaker 1')
+                tts_voice01 = gr.inputs.Dropdown(list_tts, default='en-CA-ClaraNeural-Female', label = 'TTS Speaker 2')
+                tts_voice02 = gr.inputs.Dropdown(list_tts, default='en-GB-ThomasNeural-Male', label = 'TTS Speaker 3')
+                tts_voice03 = gr.inputs.Dropdown(list_tts, default='en-GB-SoniaNeural-Female', label = 'TTS Speaker 4')
+                tts_voice04 = gr.inputs.Dropdown(list_tts, default='en-NZ-MitchellNeural-Male', label = 'TTS Speaker 5')
+                tts_voice05 = gr.inputs.Dropdown(list_tts, default='en-GB-MaisieNeural-Female', label = 'TTS Speaker 6')
+    
+                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")  
+        
+            with gr.Column(variant='compact'): 
+                with gr.Row():
+                    video_button = gr.Button("Translate audio of video", )
+                with gr.Row():
+                    video_output = gr.Video()
+
+
+                gr.Examples(
+                    examples=[
+                        [
+                            "./assets/Video_subtitled.mp4",
+                            "base",
+                            16,
+                            "float32",
+                            "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',
+                        ],
+                    ],
+                    fn=translate_from_video,
+                    inputs=[
+                    video_input,
+                    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,
+                    ],
+                    outputs=[video_output],
+                    #cache_examples=True,
+                )
+
+
+    with gr.Tab("Translate audio from video link"):
+        with gr.Row():
+            with gr.Column():
+                
+                link_input = gr.Textbox(label="Media link. Example: www.youtube.com/watch?v=g_9rPvbENUw", placeholder="URL goes here...")
+                #filename = gr.Textbox(label="File name", placeholder="best-vid")
+                
+                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')
+        
+                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")
+                
+                # 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 audio of video")
+                with gr.Row():
+                    link_output = gr.Video() #gr.outputs.File(label="Download!") # gr.Video()
+
+                gr.Examples(
+                    examples=[
+                        [
+                            "https://www.youtube.com/watch?v=5ZeHtRKHl7Y",
+                            "base",
+                            16,
+                            "float32",
+                            "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',
+                        ],
+                    ],
+                    fn=translate_from_video,
+                    inputs=[
+                    link_input,
+                    bWHISPER_MODEL_SIZE, 
+                    bbatch_size,
+                    bcompute_type, 
+                    bTRANSLATE_AUDIO_TO, 
+                    bmin_speakers,
+                    bmax_speakers,
+                    btts_voice00,
+                    btts_voice01,
+                    btts_voice02,
+                    btts_voice03,
+                    btts_voice04,
+                    btts_voice05,
+                    ],
+                    outputs=[video_output],
+                    #cache_examples=True,
+                )
+
+
+    
+    with gr.Accordion("Logs"):
+        logs = gr.Textbox()
+        demo.load(read_logs, None, logs, every=1)
+
+    # run
+    video_button.click(translate_from_video, inputs=[
+        video_input, 
+        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,], outputs=video_output)
+    text_button.click(translate_from_video, inputs=[
+        link_input,
+        bWHISPER_MODEL_SIZE, 
+        bbatch_size,
+        bcompute_type, 
+        bTRANSLATE_AUDIO_TO, 
+        bmin_speakers,
+        bmax_speakers,
+        btts_voice00,
+        btts_voice01,
+        btts_voice02,
+        btts_voice03,
+        btts_voice04,
+        btts_voice05,], outputs=link_output)
+
+
+demo.launch(enable_queue=True)
+
+
+
+

requirements.txt

@@ -0,0 +1,11 @@
+torch
+torchvision
+git+https://github.com/m-bain/whisperx.git
+yt-dlp
+gTTS
+pydub
+edge_tts
+deep_translator
+torchaudio==2.0.0
+gradio
+nest_asyncio

soni_translate/audio_segments.py

@@ -0,0 +1,27 @@
+from pydub import AudioSegment
+from tqdm import tqdm
+import os
+
+def create_translated_audio(result_diarize, audio_files, Output_name_file):
+    total_duration = result_diarize['segments'][-1]['end'] # in seconds
+
+    # silent audio with total_duration
+    combined_audio = AudioSegment.silent(duration=int(total_duration * 1000))
+    print(round((total_duration / 60),2), 'minutes of video')
+
+    for line, audio_file in tqdm(zip(result_diarize['segments'], audio_files)):
+        start = float(line['start'])
+
+        # Overlay each audio at the corresponding time
+        try:
+          audio = AudioSegment.from_file(audio_file)
+          ###audio_a = audio.speedup(playback_speed=1.5)
+          start_time = start * 1000  # to ms
+          combined_audio = combined_audio.overlay(audio, position=start_time)
+        except:
+          print(f'ERROR AUDIO FILE {audio_file}')
+
+    os.system("rm -rf audio/*")
+
+    # combined audio as a file
+    combined_audio.export(Output_name_file, format="wav")

soni_translate/text_to_speech.py

@@ -0,0 +1,30 @@
+from gtts import gTTS
+import edge_tts
+import asyncio
+import nest_asyncio
+
+def make_voice(tts_text, tts_voice, filename):
+    try:
+      nest_asyncio.apply()
+      asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(filename))
+    except 1:
+      tts = gTTS(text, lang=TRANSLATE_AUDIO_TO)
+      tts.save(filename)
+      print('USE GTTS')
+    except 2:
+      tts = gTTS('a', lang=TRANSLATE_AUDIO_TO)
+      tts.save(filename)
+      print('REPLACE AUDIO GTTS')
+
+def make_voice_gradio(tts_text, tts_voice, filename):
+    print(tts_text, filename)
+    try:
+      asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(filename))
+    except 1:
+      tts = gTTS(text, lang=TRANSLATE_AUDIO_TO)
+      tts.save(filename)
+      print('USE GTTS')
+    except 2:
+      tts = gTTS('a', lang=TRANSLATE_AUDIO_TO)
+      tts.save(filename)
+      print('REPLACE AUDIO GTTS')

soni_translate/translate_segments.py

@@ -0,0 +1,10 @@
+from tqdm import tqdm
+from deep_translator import GoogleTranslator
+
+def translate_text(segments, TRANSLATE_AUDIO_TO):
+    for line in tqdm(range(len(segments))):
+        text = segments[line]['text']
+        translator = GoogleTranslator(source='auto', target=TRANSLATE_AUDIO_TO)
+        translated_line = translator.translate(text.strip())
+        segments[line]['text'] = translated_line
+    return segments