app.py

import spaces
import gradio as gr
from faster_whisper import WhisperModel
import logging
import os
import pysrt
import pandas as pd
from transformers import MarianMTModel, MarianTokenizer
import ffmpeg
import torch

# Configuration initiale et chargement des données
url = "https://huggingface.co/Lenylvt/LanguageISO/resolve/main/iso.md"
df = pd.read_csv(url, delimiter="|", skiprows=2, header=None).dropna(axis=1, how='all')
df.columns = ['ISO 639-1', 'ISO 639-2', 'Language Name', 'Native Name']
df['ISO 639-1'] = df['ISO 639-1'].str.strip()

language_options = [(row['ISO 639-1'], f"{row['ISO 639-1']}") for index, row in df.iterrows()]
model_size_options = ["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"]  # Add model size options

logging.basicConfig(level=logging.DEBUG)

# Fonction pour formater un texte en SRT
def text_to_srt(text):
    lines = text.split('\n')
    srt_content = ""
    for i, line in enumerate(lines):
        if line.strip() == "":
            continue
        try:
            times, content = line.split(']', 1)
            start, end = times[1:].split(' -> ')
            if start.count(":") == 1:
                start = "00:" + start
            if end.count(":") == 1:
                end = "00:" + end
            srt_content += f"{i+1}\n{start.replace('.', ',')} --> {end.replace('.', ',')}\n{content.strip()}\n\n"
        except ValueError:
            continue
    temp_file_path = '/tmp/output.srt'
    with open(temp_file_path, 'w', encoding='utf-8') as file:
        file.write(srt_content)
    return temp_file_path
    
# Fonction pour formater des secondes en timestamp
def format_timestamp(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    seconds_remainder = seconds % 60
    return f"{hours:02d}:{minutes:02d}:{seconds_remainder:06.3f}"
    
@spaces.GPU
# Fonction de traduction de texte
def translate_text(text, source_language_code, target_language_code):
    model_name = f"Helsinki-NLP/opus-mt-{source_language_code}-{target_language_code}"
    if source_language_code == target_language_code:
        return "Translation between the same languages is not supported."
    try:
        tokenizer = MarianTokenizer.from_pretrained(model_name)
        model = MarianMTModel.from_pretrained(model_name)
    except Exception as e:
        return f"Failed to load model for {source_language_code} to {target_language_code}: {str(e)}"
    translated = model.generate(**tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512))
    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
    return translated_text
    
@spaces.GPU
# Fonction pour traduire un fichier SRT
def translate_srt(input_file_path, source_language_code, target_language_code, progress=None):
    subs = pysrt.open(input_file_path)
    translated_subs = []
    for idx, sub in enumerate(subs):
        translated_text = translate_text(sub.text, source_language_code, target_language_code)
        translated_sub = pysrt.SubRipItem(index=idx+1, start=sub.start, end=sub.end, text=translated_text)
        translated_subs.append(translated_sub)
        if progress:
            progress((idx + 1) / len(subs))
    translated_srt_path = input_file_path.replace(".srt", f"_{target_language_code}.srt")
    pysrt.SubRipFile(translated_subs).save(translated_srt_path)
    return translated_srt_path
    
@spaces.GPU
# Fonction pour transcrire l'audio d'une vidéo en texte
def transcribe(audio_file_path, model_size="base"):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    compute_type = "float16" if device == "cuda" else "int8"
    model = WhisperModel(model_size, device=device, compute_type=compute_type)
    segments, _ = model.transcribe(audio_file_path)
    transcription_with_timestamps = [
        f"[{format_timestamp(segment.start)} -> {format_timestamp(segment.end)}] {segment.text}"
        for segment in segments
    ]
    return "\n".join(transcription_with_timestamps)
    
@spaces.GPU
# Fonction pour ajouter des sous-titres à une vidéo
def add_subtitle_to_video(input_video, subtitle_file, subtitle_language, soft_subtitle=False):
    video_input_stream = ffmpeg.input(input_video)
    subtitle_input_stream = ffmpeg.input(subtitle_file)
    input_video_name = os.path.splitext(os.path.basename(input_video))[0]
    output_video = f"/tmp/{input_video_name}_subtitled.mp4"
    if soft_subtitle:
        stream = ffmpeg.output(video_input_stream, subtitle_input_stream, output_video, **{"c": "copy", "c:s": "mov_text"})
    else:
        stream = ffmpeg.output(video_input_stream, output_video, vf=f"subtitles={subtitle_file}")
    ffmpeg.run(stream, overwrite_output=True)
    return output_video

# Initialisation de Gradio Blocks
with gr.Blocks() as blocks_app:
    gr.Markdown(
    """
    # Video Subtitle Creation API
    For web use please visit [this space](https://huggingface.co/spaces/Lenylvt/VideoSubtitleCreation)
    """)
    with gr.Row():
        video_file = gr.Video(label="Upload Video")
        source_language_dropdown = gr.Dropdown(choices=language_options, label="Source Language", value="en")
        target_language_dropdown = gr.Dropdown(choices=language_options, label="Target Language", value="en")
        model_size_dropdown = gr.Dropdown(choices=model_size_options, label="Model Size", value="large")  # Model size dropdown
        transcribe_button = gr.Button("Transcribe Video")
        translate_button = gr.Button("Translate Subtitles")
    
    output_video = gr.Video(label="Processed Video")
    output_srt = gr.File(label="Subtitles File (.srt)")

    def transcribe_and_add_subtitles(video_file, model_size):
        transcription = transcribe(video_file, model_size)
        srt_path = text_to_srt(transcription)
        output_video_path = add_subtitle_to_video(video_file, srt_path, subtitle_language="eng", soft_subtitle=False)
        return output_video_path, srt_path
    
    def translate_subtitles_and_add_to_video(video_file, source_language_code, target_language_code, model_size):
        transcription = transcribe(video_file, model_size)
        srt_path = text_to_srt(transcription)
        translated_srt_path = translate_srt(srt_path, source_language_code, target_language_code)
        output_video_path = add_subtitle_to_video(video_file, translated_srt_path, target_language_code, soft_subtitle=False)
        return output_video_path, translated_srt_path
    
    transcribe_button.click(transcribe_and_add_subtitles, inputs=[video_file, model_size_dropdown], outputs=[output_video, output_srt])
    translate_button.click(translate_subtitles_and_add_to_video, inputs=[video_file, source_language_dropdown, target_language_dropdown, model_size_dropdown], outputs=[output_video, output_srt])

# Lancement de l'application
blocks_app.launch()