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Zero
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import logging
import torch
import torchaudio
from transformers import pipeline
class WhisperWrapper:
"""Simplified Whisper ASR wrapper"""
def __init__(self, model_id="openai/whisper-large-v3"):
"""
Initialize WhisperWrapper
Args:
model_id: Whisper model ID, default uses openai/whisper-large-v3
"""
self.logger = logging.getLogger(__name__)
self.model = None
try:
self.model = pipeline("automatic-speech-recognition", model=model_id)
self.logger.info(f"✓ Whisper model loaded successfully: {model_id}")
except Exception as e:
self.logger.error(f"❌ Failed to load Whisper model: {e}")
raise
def __call__(self, audio_input):
"""
Audio to text transcription
Args:
audio_input: Audio file path or audio tensor
Returns:
Transcribed text
"""
if self.model is None:
raise RuntimeError("Whisper model not loaded")
try:
# Load audio
if isinstance(audio_input, str):
# Audio file path
audio, audio_sr = torchaudio.load(audio_input)
audio = torchaudio.functional.resample(audio, audio_sr, 16000)
# Handle stereo to mono conversion (pipeline may not handle this)
if audio.shape[0] > 1:
audio = audio.mean(dim=0, keepdim=True) # Convert stereo to mono by averaging
# Convert to numpy and squeeze
audio = audio.squeeze(0).numpy()
elif isinstance(audio_input, torch.Tensor):
# Tensor input
audio = audio_input.cpu()
audio = torchaudio.functional.resample(audio, audio_sr, 16000)
# Handle stereo to mono conversion
if audio.ndim > 1 and audio.shape[0] > 1:
audio = audio.mean(dim=0, keepdim=True)
audio = audio.squeeze().numpy()
else:
raise ValueError(f"Unsupported audio input type: {type(audio_input)}")
# Transcribe
result = self.model(audio)
text = result.get("text", "").strip() if isinstance(result, dict) else str(result).strip()
self.logger.debug(f"Transcription result: {text}")
return text
except Exception as e:
self.logger.error(f"Audio transcription failed: {e}")
return ""
def is_available(self):
"""Check if whisper model is available"""
return self.model is not None |