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import gradio as gr
import subprocess
import whisper
from transformers import pipeline
from TTS.api import TTS # Coqui TTS library
from moviepy.editor import VideoFileClip, AudioFileClip
# 1. Download Audio and Video from YouTube
def download_audio_video(youtube_url, audio_path="audio.mp3", video_path="input_video.mp4"):
# Download audio as MP3
audio_command = [
"yt-dlp",
"--extract-audio",
"--audio-format", "mp3",
"--output", audio_path,
youtube_url
]
subprocess.run(audio_command)
# Download video as MP4
video_command = [
"yt-dlp",
"--format", "mp4",
"--output", video_path,
youtube_url
]
subprocess.run(video_command)
# 2. Transcribe Audio Using Whisper
def transcribe_audio(audio_path, model_name="base"):
model = whisper.load_model(model_name)
result = model.transcribe(audio_path)
transcription = result['text']
print(f"Whisper Transcription:\n{transcription}\n") # Print the transcription
return transcription
# 3. Split Text for Translation
def split_text(text, max_length=400):
"""
Splits the text into chunks of `max_length` words.
"""
words = text.split()
chunks = []
current_chunk = []
for word in words:
if len(" ".join(current_chunk + [word])) > max_length:
chunks.append(" ".join(current_chunk))
current_chunk = [word]
else:
current_chunk.append(word)
if current_chunk:
chunks.append(" ".join(current_chunk))
return chunks
# 4. Translate Text to Turkish
def translate_to_turkish(text, model_name="Helsinki-NLP/opus-mt-tc-big-en-tr", max_length=400):
translator = pipeline("translation", model=model_name)
# Split the text into manageable chunks
text_chunks = split_text(text, max_length=max_length)
translated_chunks = []
for chunk in text_chunks:
translated = translator(chunk, max_length=max_length)
translated_chunks.append(translated[0]['translation_text'])
full_translation = " ".join(translated_chunks)
print(f"Translated Text (English to Turkish):\n{full_translation}\n") # Print the translated text
return full_translation
# 5. Synthesize Turkish Audio Using Coqui TTS
def synthesize_audio(text, model_name="tts_models/multilingual/multi-dataset/xtts_v2", output_path="output.wav"):
# Initialize the TTS model
tts = TTS(model_name=model_name, gpu=False) # Use CPU only
# Generate and save the audio
tts.tts_to_file(text=text, file_path=output_path, speaker_wav ="audio.mp3" , language="tr")
return output_path
# 6. Replace Audio in Downloaded Video
def replace_audio(video_path, new_audio_path, output_path="translated_video.mp4"):
video = VideoFileClip(video_path)
audio = AudioFileClip(new_audio_path)
final_video = video.set_audio(audio)
final_video.write_videofile(output_path, codec="libx264", audio_codec="aac")
return output_path
# Gradio Interface
def translate_pipeline(youtube_url):
# Define file paths
audio_path = "audio.mp3"
video_path = "input_video.mp4"
# Step 1: Download audio and video
download_audio_video(youtube_url, audio_path, video_path)
# Step 2: Transcribe audio
english_text = transcribe_audio(audio_path)
# Step 3: Translate to Turkish
turkish_text = translate_to_turkish(english_text)
# Step 4: Synthesize Turkish audio
synthesized_audio = synthesize_audio(turkish_text)
# Step 5: Replace audio in the downloaded video
translated_video = replace_audio(video_path, synthesized_audio, "translated_video.mp4")
return translated_video
# Define Gradio interface
iface = gr.Interface(
fn=translate_pipeline,
inputs=gr.Textbox(label="YouTube URL"),
outputs=gr.Video(label="Translated Video"),
)
iface.launch(debug=True)
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