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122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
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
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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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config_path = os.path.join(os.path.dirname(__file__), "config.ini")
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config.read(config_path)
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return config
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# Load configuration
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config = load_config()
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# Whisper configuration
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DEFAULT_MODEL = config["whisper"]["default_model"]
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DEVICE = config["whisper"]["device"] if torch.cuda.is_available() else "cpu"
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COMPUTE_TYPE = config["whisper"]["compute_type"] if DEVICE == "cuda" else "float32"
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BEAM_SIZE = config["whisper"].getint("beam_size")
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VAD_FILTER = config["whisper"].getboolean("vad_filter")
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# App configuration
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MAX_DURATION = config["app"].getint("max_duration")
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SERVER_NAME = config["app"]["server_name"]
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SERVER_PORT = config["app"].getint("server_port")
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SHARE = config["app"].getboolean("share")
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# Available models and languages
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WHISPER_MODELS = config["models"]["available_models"].split(",")
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AVAILABLE_LANGUAGES = config["languages"]["available_languages"].split(",")
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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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return WhisperModel(model_name, device=DEVICE, compute_type=COMPUTE_TYPE)
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def transcribe_audio(
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audio_file: str, model_name: str, language: str = None
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) -> tuple[str, str]:
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"""Transcribe audio using the selected Whisper model."""
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try:
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# Load the model
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model = load_model(model_name)
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# Transcribe the audio
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segments, info = 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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return full_text, info.language
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except Exception as e:
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return f"Error during transcription: {str(e)}", None
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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("Upload an audio or video file to transcribe it using Whisper AI.")
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with gr.Row():
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with gr.Column():
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# Input components
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audio_input = gr.Audio(
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label="Upload Audio/Video", type="filepath", format="mp3"
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)
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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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)
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language_dropdown = gr.Dropdown(
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choices=["Auto-detect"] + AVAILABLE_LANGUAGES,
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value="Auto-detect",
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label="Language (optional)",
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)
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transcribe_btn = gr.Button("Transcribe", variant="primary")
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with gr.Column():
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# Output components
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output_text = gr.Textbox(label="Transcription", lines=10, max_lines=20)
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detected_language = gr.Textbox(
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label="Detected Language", interactive=False
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)
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# Set up the event handler
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transcribe_btn.click(
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fn=transcribe_audio,
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inputs=[audio_input, model_dropdown, language_dropdown],
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outputs=[output_text, detected_language],
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)
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# Add some helpful information
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gr.Markdown(
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f"""
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### Tips:
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- For better accuracy, use larger models (medium, large)
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- Processing time increases with model size
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- GPU is recommended for faster processing
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- Maximum audio duration is {MAX_DURATION // 60} minutes
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"""
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)
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return app
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if __name__ == "__main__":
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app = create_interface()
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app.launch(share=SHARE, server_name=SERVER_NAME, server_port=SERVER_PORT)
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