mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-10-03 00:55:14 -07:00
Refactor App and Sidebar components to support macOS, add TitleBarDragRegion for improved window dragging, and enhance model management with delete functionality and download progress tracking. Update audio player to handle audio resets more effectively and improve generation form with model download notifications.
This commit is contained in:
Jamie Pine
+102
-81
@@ -3,6 +3,7 @@ Whisper ASR module for transcription.
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"""
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from typing import Optional, List, Dict
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import asyncio
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import torch
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import numpy as np
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from pathlib import Path
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@@ -79,6 +80,22 @@ class WhisperModel:
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progress_manager.mark_error(f"whisper-{model_size}", str(e))
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raise
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async def load_model_async(self, model_size: Optional[str] = None):
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"""
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Async version of load_model that runs in thread pool.
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This prevents blocking the event loop during model loading.
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"""
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if model_size is None:
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model_size = self.model_size
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# If already loaded with correct size, return immediately
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if self.model is not None and self.model_size == model_size:
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return
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# Run the blocking load operation in a thread pool
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await asyncio.to_thread(self.load_model, model_size)
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def unload_model(self):
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"""Unload the model to free memory."""
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if self.model is not None:
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@@ -107,44 +124,49 @@ class WhisperModel:
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Returns:
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Transcribed text
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"""
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self.load_model()
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await self.load_model_async()
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from .utils.audio import load_audio
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# Load audio
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audio, sr = load_audio(audio_path, sample_rate=16000)
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# Process audio
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inputs = self.processor(
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audio,
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sampling_rate=16000,
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return_tensors="pt",
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)
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inputs = inputs.to(self.device)
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# Set language if provided
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forced_decoder_ids = None
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if language:
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lang_code = "en" if language == "en" else "zh"
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forced_decoder_ids = self.processor.get_decoder_prompt_ids(
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language=lang_code,
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task="transcribe",
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def _transcribe_sync():
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"""Run synchronous transcription in thread pool."""
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# Load audio
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audio, sr = load_audio(audio_path, sample_rate=16000)
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# Process audio
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inputs = self.processor(
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audio,
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sampling_rate=16000,
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return_tensors="pt",
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)
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inputs = inputs.to(self.device)
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# Set language if provided
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forced_decoder_ids = None
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if language:
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lang_code = "en" if language == "en" else "zh"
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forced_decoder_ids = self.processor.get_decoder_prompt_ids(
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language=lang_code,
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task="transcribe",
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)
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# Generate transcription
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with torch.no_grad():
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predicted_ids = self.model.generate(
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inputs["input_features"],
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forced_decoder_ids=forced_decoder_ids,
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)
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# Decode
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transcription = self.processor.batch_decode(
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predicted_ids,
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skip_special_tokens=True,
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)[0]
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return transcription.strip()
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# Generate transcription
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with torch.no_grad():
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predicted_ids = self.model.generate(
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inputs["input_features"],
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forced_decoder_ids=forced_decoder_ids,
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)
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# Decode
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transcription = self.processor.batch_decode(
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predicted_ids,
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skip_special_tokens=True,
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)[0]
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return transcription.strip()
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# Run blocking transcription in thread pool
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return await asyncio.to_thread(_transcribe_sync)
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async def transcribe_with_timestamps(
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self,
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@@ -161,59 +183,58 @@ class WhisperModel:
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Returns:
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List of word segments with timestamps
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"""
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self.load_model()
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await self.load_model_async()
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from .utils.audio import load_audio
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# Load audio
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audio, sr = load_audio(audio_path, sample_rate=16000)
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# Process audio
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inputs = self.processor(
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audio,
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sampling_rate=16000,
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return_tensors="pt",
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)
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inputs = inputs.to(self.device)
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# Set language if provided
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forced_decoder_ids = None
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if language:
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lang_code = "en" if language == "en" else "zh"
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forced_decoder_ids = self.processor.get_decoder_prompt_ids(
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language=lang_code,
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task="transcribe",
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def _transcribe_timestamps_sync():
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"""Run synchronous transcription with timestamps in thread pool."""
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# Load audio
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audio, sr = load_audio(audio_path, sample_rate=16000)
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# Process audio
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inputs = self.processor(
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audio,
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sampling_rate=16000,
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return_tensors="pt",
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)
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inputs = inputs.to(self.device)
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# Set language if provided
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forced_decoder_ids = None
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if language:
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lang_code = "en" if language == "en" else "zh"
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forced_decoder_ids = self.processor.get_decoder_prompt_ids(
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language=lang_code,
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task="transcribe",
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)
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# Generate with timestamps
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with torch.no_grad():
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predicted_ids = self.model.generate(
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inputs["input_features"],
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forced_decoder_ids=forced_decoder_ids,
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return_timestamps=True,
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)
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# Parse timestamps (simplified - would need more robust parsing)
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# For now, return basic transcription
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# TODO: Implement proper timestamp parsing
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transcription = self.processor.batch_decode(
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predicted_ids,
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skip_special_tokens=True,
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)[0]
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return [
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{
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"text": transcription,
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"start": 0.0,
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"end": len(audio) / sr,
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}
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]
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# Generate with timestamps
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with torch.no_grad():
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predicted_ids = self.model.generate(
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inputs["input_features"],
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forced_decoder_ids=forced_decoder_ids,
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return_timestamps=True,
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)
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# Decode with timestamps
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result = self.processor.batch_decode(
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predicted_ids,
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skip_special_tokens=False,
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)[0]
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# Parse timestamps (simplified - would need more robust parsing)
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# For now, return basic transcription
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# TODO: Implement proper timestamp parsing
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transcription = self.processor.batch_decode(
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predicted_ids,
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skip_special_tokens=True,
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)[0]
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return [
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{
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"text": transcription,
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"start": 0.0,
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"end": len(audio) / sr,
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}
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]
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# Run blocking transcription in thread pool
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return await asyncio.to_thread(_transcribe_timestamps_sync)
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# Global model instance
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