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import os
import math
import gradio as gr
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from moviepy.editor import VideoFileClip, concatenate_audioclips
def transcribe(video_file, transcribe_to_text, transcribe_to_srt, language):
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=10, # Shorter chunk length to prevent overly long captions
batch_size=2,
return_timestamps=True,
torch_dtype=torch_dtype,
device=device,
)
if video_file is None:
yield "Error: No video file provided.", None
return
video_path = video_file.name if hasattr(video_file, 'name') else video_file
try:
video = VideoFileClip(video_path)
except Exception as e:
yield f"Error processing video file: {str(e)}", None
return
audio = video.audio
duration = video.duration
n_chunks = math.ceil(duration / 10) # Split into 10-second chunks
transcription_txt = ""
transcription_srt = []
for i in range(n_chunks):
start = i * 10
end = min((i + 1) * 10, duration)
audio_chunk = audio.subclip(start, end)
temp_file_path = f"temp_audio_{i}.wav"
audio_chunk.write_audiofile(temp_file_path, codec='pcm_s16le')
with open(temp_file_path, "rb") as temp_file:
result = pipe(temp_file_path, generate_kwargs={"language": language})
transcription_txt += result["text"]
if transcribe_to_srt:
for chunk in result["chunks"]:
start_time, end_time = chunk["timestamp"]
if start_time is not None and end_time is not None:
transcription_srt.append({
"start": start_time + i * 10,
"end": end_time + i * 10,
"text": chunk["text"]
})
else:
print(f"Warning: Invalid timestamp for chunk: {chunk}")
os.remove(temp_file_path)
yield f"Progress: {int(((i + 1) / n_chunks) * 100)}%", None
output = ""
srt_file_path = None
if transcribe_to_text:
output += "Text Transcription:\n" + transcription_txt + "\n\n"
if transcribe_to_srt:
output += "SRT Transcription:\n"
srt_content = ""
for i, sub in enumerate(transcription_srt, 1):
srt_entry = f"{i}\n{format_time(sub['start'])} --> {format_time(sub['end'])}\n{sub['text']}\n\n"
output += srt_entry
srt_content += srt_entry
srt_file_path = "transcription.srt"
with open(srt_file_path, "w", encoding="utf-8") as srt_file:
srt_file.write(srt_content)
output += f"\nSRT file saved as: {srt_file_path}"
yield output, srt_file_path
def format_time(seconds):
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
return f"{int(h):02d}:{int(m):02d}:{s:06.3f}".replace('.', ',')
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.Video(),
gr.Checkbox(label="Transcribe to Text"),
gr.Checkbox(label="Transcribe to SRT"),
gr.Dropdown(choices=['en', 'he', 'it', 'es', 'fr', 'de', 'zh', 'ar'], label="Language")
],
outputs=[
gr.Textbox(label="Transcription Output"),
gr.File(label="Download SRT")
],
title="WhisperCap Video Transcription",
description="Upload a video file to transcribe its audio using Whisper. You can download the SRT file if generated.",
)
iface.launch(share=True)
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