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import whisper
from transformers import MarianMTModel, MarianTokenizer, AutoTokenizer, AutoModelForSeq2SeqLM
import os
import tempfile
import subprocess
# Load Whisper model
model = whisper.load_model("base")
def process_video(video_path, language, progress=None):
output_video_path = os.path.join(tempfile.gettempdir(), "converted_video.mp4")
srt_path = os.path.join(tempfile.gettempdir(), "subtitles.srt")
try:
# Convert video to MP4 using ffmpeg
if progress:
progress(0.2, desc="🔄 Converting video to MP4...")
subprocess.run(
["ffmpeg", "-i", video_path, "-c:v", "libx264", "-preset", "fast", output_video_path],
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
# Transcribe video
if progress:
progress(0.4, desc="📝 Transcribing audio...")
result = model.transcribe(output_video_path, language="en")
if progress:
progress(0.6, desc="🌐 Translating subtitles...")
# Translation logic
segments = []
if language == "English":
segments = result["segments"]
else:
model_map = {
"Hindi": "Helsinki-NLP/opus-mt-en-hi",
"Spanish": "Helsinki-NLP/opus-mt-en-es",
"French": "Helsinki-NLP/opus-mt-en-fr",
"German": "Helsinki-NLP/opus-mt-en-de",
"Telugu": "facebook/nllb-200-distilled-600M",
"Portuguese": "Helsinki-NLP/opus-mt-en-pt",
"Russian": "Helsinki-NLP/opus-mt-en-ru",
"Chinese": "Helsinki-NLP/opus-mt-en-zh",
"Arabic": "Helsinki-NLP/opus-mt-en-ar",
"Japanese": "Helsinki-NLP/opus-mt-en-jap"
}
model_name = model_map.get(language)
if not model_name:
return None
# Load translation model
if language == "Telugu":
tokenizer = AutoTokenizer.from_pretrained(model_name)
translation_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tgt_lang = "tel_Telu"
for segment in result["segments"]:
inputs = tokenizer(segment["text"], return_tensors="pt", padding=True)
translated_tokens = translation_model.generate(
**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang)
)
translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
segments.append({"text": translated_text, "start": segment["start"], "end": segment["end"]})
else:
tokenizer = MarianTokenizer.from_pretrained(model_name)
translation_model = MarianMTModel.from_pretrained(model_name)
for segment in result["segments"]:
inputs = tokenizer(segment["text"], return_tensors="pt", padding=True)
translated = translation_model.generate(**inputs)
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
segments.append({"text": translated_text, "start": segment["start"], "end": segment["end"]})
# Create SRT file
if progress:
progress(0.8, desc="📝 Generating SRT file...")
with open(srt_path, "w", encoding="utf-8") as f:
for i, segment in enumerate(segments, 1):
start = f"{segment['start']:.3f}".replace(".", ",")
end = f"{segment['end']:.3f}".replace(".", ",")
text = segment["text"].strip()
f.write(f"{i}\n00:00:{start} --> 00:00:{end}\n{text}\n\n")
if progress:
progress(1.0, desc="✅ Done!")
return srt_path
except subprocess.CalledProcessError as e:
print(f"FFmpeg Error: {e.stderr.decode()}")
return None
except Exception as e:
print(f"Unexpected Error: {str(e)}")
return None
finally:
# Clean up temporary files
if os.path.exists(output_video_path):
os.remove(output_video_path)
if os.path.exists(video_path):
os.remove(video_path) |