wav2vecasr/demo.py

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from speechbrain.pretrained import GraphemeToPhoneme
import datasets
import os
import torchaudio
from MispronounciationDetector import MispronounciationDetector

# Load sample data
audio_path, transcript_path = os.path.join(os.getcwd(), "data", "arctic_a0003.wav"), os.path.join(os.getcwd(), "data", "arctic_a0003.txt")
audio, org_sr = torchaudio.load(audio_path)
audio = torchaudio.functional.resample(audio, orig_freq=org_sr, new_freq=16000)
audio = audio.view(audio.shape[1])
with open(transcript_path) as f:
  text = f.read()
f.close()
print("Done loading sample data")

# Load processors and models
device = "cpu"
path = os.path.join(os.getcwd(), "model", "checkpoint-1200")
model = Wav2Vec2ForCTC.from_pretrained(path).to(device)
processor = Wav2Vec2Processor.from_pretrained(path)
g2p = GraphemeToPhoneme.from_hparams("speechbrain/soundchoice-g2p")
mispronounciation_detector = MispronounciationDetector(model, processor, g2p, "cpu")
print("Done loading models and processors")

# Predict
raw_info = mispronounciation_detector.detect(audio, text)
aligned_phoneme_output_delimited_by_words = " ".join(raw_info['ref']) + "\n" + " ".join(raw_info['hyp']) + "\n" +\
                                            " ".join(raw_info['phoneme_errors'])
print(f"PER: {raw_info['per']}\n")
print(f"Phoneme level errors:\n{raw_info['phoneme_output']}\n")