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import whisper
from transformers import MarianMTModel, MarianTokenizer, AutoTokenizer, AutoModelForSeq2SeqLM
import os
# Load Whisper model
model = whisper.load_model("base")
def process_video(video_file, language):
# Save uploaded video locally
video_path = "/tmp/video.mp4"
with open(video_path, "wb") as f:
f.write(video_file.read())
try:
print("Transcribing video to English...")
result = model.transcribe(video_path, language="en")
segments = []
if language == "English":
segments = result["segments"]
else:
if language == "Telugu":
model_name = "facebook/nllb-200-distilled-600M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
translation_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tgt_lang = "tel_Telu"
print(f"Translating to Telugu using NLLB-200 Distilled...")
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:
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",
"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[language]
tokenizer = MarianTokenizer.from_pretrained(model_name)
translation_model = MarianMTModel.from_pretrained(model_name)
print(f"Translating to {language}...")
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
srt_path = "/tmp/subtitles.srt"
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")
return srt_path
except Exception as e:
return f"Error: {str(e)}" |