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import librosa
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
import logging
# Set up logging
logging.basicConfig(level=logging.DEBUG)
ASR_SAMPLING_RATE = 16_000
MODEL_ID = "facebook/wav2vec2-large-960h-lv60-self"
try:
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
logging.info("ASR model and processor loaded successfully.")
except Exception as e:
logging.error(f"Error loading ASR model or processor: {e}")
def transcribe(audio):
try:
if audio is None:
logging.error("No audio file provided")
return "ERROR: You have to either use the microphone or upload an audio file"
logging.info(f"Loading audio file: {audio}")
audio_samples, _ = librosa.load(audio, sr=ASR_SAMPLING_RATE, mono=True)
inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = inputs.to(device)
with torch.no_grad():
outputs = model(**inputs).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
logging.info("Transcription completed successfully.")
return transcription
except Exception as e:
logging.error(f"Error during transcription: {e}")
return "ERROR"
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