mirror of
https://github.com/tcsenpai/whisperapp.git
synced 2025-06-05 14:45:20 +00:00
switched to whisperX
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tcsenpai
parent
f7d26a2325
commit
4ad72ffe8d
16
README.md
16
README.md
@ -14,9 +14,10 @@ A user-friendly web application for transcribing audio and video files using Ope
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## Requirements
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- Python 3.8+
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- Python 3.10+
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- CUDA-capable GPU (recommended)
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- FFmpeg (for audio/video processing)
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- uv package manager
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## Installation
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@ -26,17 +27,16 @@ git clone <repository-url>
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cd whisperapp
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```
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2. Create a virtual environment and activate it:
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows: venv\Scripts\activate
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```
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3. Install uv (recommended package installer):
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2. Install uv (if you just pip install you might break your environment):
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```bash
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curl -LsSf https://astral.sh/uv/install.sh | sh
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```
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3. Create a venv with uv:
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```bash
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uv venv --python=3.10
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```
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4. Install the required packages using uv:
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```bash
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uv pip install -r requirements.txt
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128
app.py
128
app.py
@ -1,12 +1,12 @@
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import os
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import gradio as gr
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from faster_whisper import WhisperModel
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import torch
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import configparser
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from typing import List, Tuple, Optional
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import youtube_handler as yt
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from ollama_handler import OllamaHandler
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import logging
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import whisperx
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import subprocess
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import sys
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@ -17,6 +17,39 @@ logging.basicConfig(
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logger = logging.getLogger(__name__)
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def check_cuda_compatibility():
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"""Check if the current CUDA setup is compatible with WhisperX."""
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logger.info("Checking CUDA compatibility...")
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# Check PyTorch CUDA
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if not torch.cuda.is_available():
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logger.warning("CUDA is not available in PyTorch")
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return False
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cuda_version = torch.version.cuda
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cudnn_version = torch.backends.cudnn.version()
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device_name = torch.cuda.get_device_name(0)
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logger.info(f"CUDA Version: {cuda_version}")
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logger.info(f"cuDNN Version: {cudnn_version}")
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logger.info(f"GPU Device: {device_name}")
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# Check CUDA version
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try:
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cuda_major = int(cuda_version.split(".")[0])
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if cuda_major > 11:
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logger.warning(
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f"CUDA {cuda_version} might not be fully compatible with WhisperX. Recommended: CUDA 11.x"
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)
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logger.info(
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"Consider creating a new environment with CUDA 11.x if you encounter issues"
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)
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except Exception as e:
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logger.error(f"Error parsing CUDA version: {str(e)}")
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return True
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def load_config() -> configparser.ConfigParser:
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"""Load configuration from config.ini file."""
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config = configparser.ConfigParser()
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@ -28,7 +61,7 @@ def load_config() -> configparser.ConfigParser:
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# Load configuration
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config = load_config()
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# Whisper configuration
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# WhisperX configuration
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DEFAULT_MODEL = config["whisper"]["default_model"]
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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COMPUTE_TYPE = "float32" # Always use float32 for better compatibility
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@ -63,11 +96,11 @@ OLLAMA_MODELS = ollama.get_available_models() if OLLAMA_AVAILABLE else []
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DEFAULT_OLLAMA_MODEL = ollama.get_default_model() if OLLAMA_AVAILABLE else None
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def load_model(model_name: str) -> WhisperModel:
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"""Load the Whisper model with the specified configuration."""
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def load_model(model_name: str) -> whisperx.WhisperModel:
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"""Load the WhisperX model with the specified configuration."""
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try:
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logger.info(f"Loading Whisper model: {model_name}")
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return WhisperModel(
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logger.info(f"Loading WhisperX model: {model_name}")
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return whisperx.load_model(
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model_name,
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device=DEVICE,
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compute_type=COMPUTE_TYPE,
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@ -76,7 +109,7 @@ def load_model(model_name: str) -> WhisperModel:
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except Exception as e:
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logger.error(f"Error loading model with CUDA: {str(e)}")
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logger.info("Falling back to CPU")
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return WhisperModel(
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return whisperx.load_model(
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model_name,
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device="cpu",
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compute_type="float32",
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@ -91,7 +124,7 @@ def transcribe_audio(
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summarize: bool = False,
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ollama_model: str = None,
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) -> tuple[str, str, Optional[str]]:
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"""Transcribe audio using the selected Whisper model."""
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"""Transcribe audio using the selected WhisperX model."""
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try:
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logger.info(f"Starting transcription of {audio_file}")
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logger.info(
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@ -103,19 +136,19 @@ def transcribe_audio(
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# Transcribe the audio
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logger.info("Starting audio transcription...")
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segments, info = model.transcribe(
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result = model.transcribe(
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audio_file,
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language=language if language != "Auto-detect" else None,
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beam_size=BEAM_SIZE,
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vad_filter=VAD_FILTER,
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)
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# Combine all segments into one text
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full_text = " ".join([segment.text for segment in segments])
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# Get the full text with timestamps
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full_text = " ".join([segment["text"] for segment in result["segments"]])
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logger.info(
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f"Transcription completed. Text length: {len(full_text)} characters"
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)
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logger.info(f"Detected language: {info.language}")
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logger.info(f"Detected language: {result['language']}")
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# Generate summary if requested
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summary = None
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@ -127,7 +160,7 @@ def transcribe_audio(
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else:
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logger.warning("Failed to generate summary")
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return full_text, info.language, summary
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return full_text, result["language"], summary
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except Exception as e:
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logger.error(f"Error during transcription: {str(e)}")
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return f"Error during transcription: {str(e)}", None, None
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@ -205,7 +238,7 @@ def process_youtube_url(
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def create_interface():
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"""Create and return the Gradio interface."""
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with gr.Blocks(theme=gr.themes.Soft()) as app:
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gr.Markdown("# 🎙️ Audio/Video Transcription with Whisper")
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gr.Markdown("# 🎙️ Audio/Video Transcription with WhisperX")
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gr.Markdown(
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"### A powerful tool for transcribing and summarizing audio/video content"
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)
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@ -233,7 +266,7 @@ def create_interface():
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yt_model_dropdown = gr.Dropdown(
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choices=WHISPER_MODELS,
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value=DEFAULT_MODEL,
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label="Select Whisper Model",
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label="Select WhisperX Model",
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)
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yt_language_dropdown = gr.Dropdown(
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choices=["Auto-detect"] + AVAILABLE_LANGUAGES,
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@ -345,7 +378,7 @@ def create_interface():
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gr.Markdown(
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"""
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### Local File Transcription
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Upload an audio or video file to transcribe it using Whisper AI.
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Upload an audio or video file to transcribe it using WhisperX.
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- Supports various audio and video formats
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- Automatic language detection
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- Optional summarization with Ollama
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@ -361,7 +394,7 @@ def create_interface():
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model_dropdown = gr.Dropdown(
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choices=WHISPER_MODELS,
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value=DEFAULT_MODEL,
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label="Select Whisper Model",
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label="Select WhisperX Model",
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)
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language_dropdown = gr.Dropdown(
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choices=["Auto-detect"] + AVAILABLE_LANGUAGES,
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@ -423,27 +456,34 @@ def create_interface():
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model = load_model(model)
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status = "Transcribing audio..."
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segments, info = model.transcribe(
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result = model.transcribe(
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audio,
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language=lang if lang != "Auto-detect" else None,
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beam_size=BEAM_SIZE,
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vad_filter=VAD_FILTER,
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)
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# Combine all segments into one text
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full_text = " ".join([segment.text for segment in segments])
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# Get the full text with timestamps
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full_text = " ".join(
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[segment["text"] for segment in result["segments"]]
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)
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if summarize and OLLAMA_AVAILABLE:
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status = "Generating summary..."
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summary = ollama.summarize(full_text, ollama_model)
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return (
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full_text,
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info.language,
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result["language"],
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summary if summary else "",
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"Processing complete!",
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)
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else:
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return full_text, info.language, "", "Processing complete!"
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return (
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full_text,
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result["language"],
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"",
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"Processing complete!",
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)
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except Exception as e:
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logger.error(f"Error in transcribe_with_summary: {str(e)}")
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@ -489,50 +529,8 @@ def create_interface():
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return app
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def check_cuda_compatibility():
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"""Check if the current CUDA setup is compatible with Whisper."""
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logger.info("Checking CUDA compatibility...")
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# Check PyTorch CUDA
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if not torch.cuda.is_available():
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logger.warning("CUDA is not available in PyTorch")
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return False
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cuda_version = torch.version.cuda
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cudnn_version = torch.backends.cudnn.version()
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device_name = torch.cuda.get_device_name(0)
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logger.info(f"CUDA Version: {cuda_version}")
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logger.info(f"cuDNN Version: {cudnn_version}")
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logger.info(f"GPU Device: {device_name}")
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# Check CUDA version
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try:
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cuda_major = int(cuda_version.split(".")[0])
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if cuda_major > 11:
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logger.warning(
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f"CUDA {cuda_version} might not be fully compatible with Whisper. Recommended: CUDA 11.x"
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)
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logger.info(
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"Consider creating a new environment with CUDA 11.x if you encounter issues"
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)
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except Exception as e:
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logger.error(f"Error parsing CUDA version: {str(e)}")
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# Check if faster-whisper is installed
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try:
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import faster_whisper
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logger.info(f"faster-whisper version: {faster_whisper.__version__}")
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except ImportError:
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logger.error("faster-whisper is not installed")
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return False
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return True
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if __name__ == "__main__":
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logger.info("Starting Whisper Transcription Web App")
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logger.info("Starting WhisperX Transcription Web App")
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# Check CUDA compatibility before starting
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if not check_cuda_compatibility():
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@ -1,9 +1,13 @@
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gradio>=4.0.0
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faster-whisper>=0.9.0
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# Choose one of these whisper implementations:
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whisperx>=3.0.0
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torch>=2.0.0,<2.1.0
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torchvision>=0.15.0,<0.16.0
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torchaudio>=2.0.0,<2.1.0
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yt-dlp>=2023.0.0
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python-dotenv>=1.0.0
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requests>=2.31.0
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ollama>=0.1.0
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ollama>=0.1.0
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# WhisperX dependencies
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ffmpeg-python>=0.2.0
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pyannote.audio>=3.1.1
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