#%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 = "📽️ SoniTranslate 🈷️"
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("
")
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("
")
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("
")
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("
")
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("
")
# 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("
")
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("
")
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("
")
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("
")
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()