| import os |
| import re |
| import shutil |
| import subprocess |
| import tempfile |
| from pathlib import Path |
|
|
| import gradio as gr |
| import torch |
| from transformers import pipeline |
|
|
| |
| |
| |
| MODEL_ID = "openai/whisper-base" |
|
|
| device = 0 if torch.cuda.is_available() else -1 |
| asr = pipeline( |
| "automatic-speech-recognition", |
| model=MODEL_ID, |
| chunk_length_s=30, |
| device=device, |
| return_timestamps=True, |
| ) |
|
|
| SUPPORTED = [".mp4", ".mov", ".mkv", ".avi", ".webm", ".m4v", ".mp3", ".wav", ".m4a", ".flac", ".ogg"] |
|
|
|
|
| |
| |
| |
| def extract_audio(video_path: str, workdir: str) -> str: |
| """Extract mono 16 kHz WAV audio from a media file using ffmpeg.""" |
| audio_path = os.path.join(workdir, "audio.wav") |
| |
| if not os.path.exists(video_path): |
| raise RuntimeError(f"ffmpeg failed: Input file '{video_path}' does not exist.") |
|
|
| cmd = [ |
| "ffmpeg", "-y", |
| "-i", video_path, |
| "-vn", |
| "-acodec", "pcm_s16le", |
| "-ar", "16000", |
| "-ac", "1", |
| audio_path, |
| ] |
| proc = subprocess.run(cmd, capture_output=True, text=True) |
| if proc.returncode != 0 or not os.path.exists(audio_path): |
| print(f"FFmpeg stderr: {proc.stderr}") |
| raise RuntimeError(f"ffmpeg failed to extract audio:\n{proc.stderr[-1500:]}") |
| return audio_path |
|
|
|
|
| def format_timestamp(seconds: float) -> str: |
| """Format seconds as SRT timestamp: HH:MM:SS,mmm""" |
| if seconds is None: |
| seconds = 0.0 |
| ms = int(round(seconds * 1000)) |
| h, ms = divmod(ms, 3600_000) |
| m, ms = divmod(ms, 60_000) |
| s, ms = divmod(ms, 1000) |
| return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}" |
|
|
|
|
| def build_srt(chunks) -> str: |
| blocks = [] |
| for i, chunk in enumerate(chunks, start=1): |
| start, end = chunk.get("timestamp", (0.0, 0.0)) |
| if end is None: |
| end = (start or 0.0) + 2.0 |
| text = chunk["text"].strip() |
| blocks.append( |
| f"{i}\n{format_timestamp(start)} --> {format_timestamp(end)}\n{text}" |
| ) |
| return "\n\n".join(blocks) + "\n" |
|
|
|
|
| def safe_stem(filename: str) -> str: |
| stem = Path(filename).stem |
| return re.sub(r"[^A-Za-z0-9_.-]+", "_", stem) or "transcript" |
|
|
|
|
| |
| |
| |
| def transcribe(media_file, language, task, progress=gr.Progress(track_tqdm=True)): |
| if media_file is None: |
| return "", "" |
|
|
| |
| |
| if isinstance(media_file, str): |
| src_path = media_file |
| else: |
| |
| print(f"Unexpected media_file type: {type(media_file)}, value: {media_file}") |
| return "Error: Invalid file input received. Please try uploading again.", "" |
|
|
| |
| if not os.path.exists(src_path): |
| print(f"File not found: {src_path}") |
| return f"Error: File not found at path: {src_path}", "" |
|
|
| ext = Path(src_path).suffix.lower() |
| if ext and ext not in SUPPORTED: |
| return f"Unsupported file type: `{ext}`", "" |
|
|
| with tempfile.TemporaryDirectory() as workdir: |
| progress(0.1, desc="Extracting audio...") |
| audio_path = extract_audio(src_path, workdir) |
|
|
| progress(0.3, desc="Transcribing (this can take a while)...") |
| kwargs = {"generate_kwargs": {"task": task}} |
| if language != "auto": |
| kwargs["generate_kwargs"]["language"] = language |
| |
| result = asr(audio_path, **kwargs) |
|
|
| text = result["text"].strip() |
| chunks = result.get("chunks") or [] |
|
|
| progress(0.9, desc="Finalizing...") |
| stem = safe_stem(src_path) |
| txt_path = os.path.join(workdir, f"{stem}_transcript.txt") |
| with open(txt_path, "w", encoding="utf-8") as f: |
| f.write(text + "\n") |
|
|
| return text, txt_path |
|
|
|
|
| |
| |
| |
| LANGUAGES = [ |
| "auto", "english", "spanish", "french", "german", "italian", "portuguese", |
| "dutch", "russian", "chinese", "japanese", "korean", "arabic", "hindi", |
| "turkish", "polish", "swedish", "ukrainian", "vietnamese", "indonesian", |
| ] |
|
|
| with gr.Blocks(title="Transcriber") as demo: |
| gr.Markdown( |
| """ |
| # 🎙️ Audio/Video Transcriber |
| Upload any audio or video file and get a clean text transcription powered by **OpenAI Whisper**. |
| **How it works** |
| 1. **Upload** an audio (.mp3, .wav, .m4a...) or video (.mp4, .mov, .mkv...) file. |
| 2. Click **Transcribe**. The audio is extracted and transcribed automatically. |
| 3. **Copy & paste** the transcript below or download it as a `.txt` file. |
| """ |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(scale=1): |
| |
| media_in = gr.Audio(label="Audio/Video", sources=["upload"], type="filepath") |
| language = gr.Dropdown( |
| choices=LANGUAGES, value="auto", |
| label="Language (optional — auto-detect by default)", |
| ) |
| task = gr.Radio( |
| choices=["transcribe", "translate"], value="transcribe", |
| label="Task ('translate' translates speech into English)", |
| ) |
| run_btn = gr.Button("🚀 Transcribe", variant="primary") |
|
|
| with gr.Column(scale=1): |
| transcript_out = gr.Textbox(label="Transcript (copy & paste)", lines=25) |
| txt_file = gr.File(label="⬇️ Download .txt") |
|
|
| run_btn.click( |
| fn=transcribe, |
| inputs=[media_in, language, task], |
| outputs=[transcript_out, txt_file], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.queue(max_size=8).launch(server_name="0.0.0.0", server_port=7860) |
