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tdns03
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tdns03
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This model assesses pronunciation quality for Korean speech. It's based on the openai/whisper-small model, fine-tuned using the Korea AI-Hub (https://www.aihub.or.kr/) foreigner Korean pronunciation evaluation dataset.
The Pronunciation Scorer takes audio input along with its corresponding text transcript and provides a Korean pronunciation score on a scale of 1 to 5. It utilizes the encoder-decoder architecture of the Whisper model to extract speech features and employs an additional linear layer to predict the pronunciation score.
To use this model, follow these steps:
Here's a detailed example of how to use the model:
text
import torchimport torchaudiofrom transformers import WhisperProcessor, WhisperForConditionalGenerationimport torch.nn as nn class WhisperPronunciationScorer(nn.Module): def __init__(self, pretrained_model): super().__init__() self.whisper = pretrained_model self.score_head = nn.Linear(self.whisper.config.d_model, 1) def forward(self, input_features, labels=None): outputs = self.whisper(input_features, labels=labels, output_hidden_states=True) last_hidden_state = outputs.decoder_hidden_states[-1] scores = self.score_head(last_hidden_state.mean(dim=1)).squeeze() return scores def load_model(model_path, device): model_name = "openai/whisper-small" processor = WhisperProcessor.from_pretrained(model_name) pretrained_model = WhisperForConditionalGeneration.from_pretrained(model_name) model = WhisperPronunciationScorer(pretrained_model).to(device) model.load_state_dict(torch.load(model_path, map_location=device)) model.eval() return model, processor def predict_pronunciation_score(model, processor, audio_path, transcript, device): # Load and preprocess audio audio, sr = torchaudio.load(audio_path) if sr != 16000: audio = torchaudio.functional.resample(audio, sr, 16000) input_features = processor(audio.squeeze().numpy(), sampling_rate=16000, return_tensors="pt").input_features.to(device) # Prepare transcript labels = processor(text=transcript, return_tensors="pt").input_ids.to(device) # Predict score with torch.no_grad(): score = model(input_features, labels) return score.item() # Load modeldevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")model_path = "path/to/your/model.pth"model, processor = load_model(model_path, device) # Run predictionaudio_path = "path/to/your/audio.wav"transcript = "안녕하세요"score = predict_pronunciation_score(model, processor, audio_path, transcript, device)print(f"Predicted pronunciation score: {score:.2f}")import torchimport torchaudiofrom transformers import WhisperProcessor, WhisperForConditionalGenerationimport torch.nn as nnclass WhisperPronunciationScorer(nn.Module): def __init__(self, pretrained_model): super().__init__() self.whisper = pretrained_model self.score_head = nn.Linear(self.whisper.config.d_model, 1) def forward(self, input_features, labels=None): outputs = self.whisper(input_features, labels=labels, output_hidden_states=True) last_hidden_state = outputs.decoder_hidden_states[-1] scores = self.score_head(last_hidden_state.mean(dim=1)).squeeze() return scoresdef load_model(model_path, device): model_name = "openai/whisper-small" processor = WhisperProcessor.from_pretrained(model_name) pretrained_model = WhisperForConditionalGeneration.from_pretrained(model_name) model = WhisperPronunciationScorer(pretrained_model).to(device) model.load_state_dict(torch.load(model_path, map_location=device)) model.eval() return model, processordef predict_pronunciation_score(model, processor, audio_path, transcript, device): # Load and preprocess audio audio, sr = torchaudio.load(audio_path) if sr != 16000: audio = torchaudio.functional.resample(audio, sr, 16000) input_features = processor(audio.squeeze().numpy(), sampling_rate=16000, return_tensors="pt").input_features.to(device) # Prepare transcript labels = processor(text=transcript, return_tensors="pt").input_ids.to(device) # Predict score with torch.no_grad(): score = model(input_features, labels) return score.item()# Load modeldevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")model_path = "path/to/your/model.pth"model, processor = load_model(model_path, device)# Run predictionaudio_path = "path/to/your/audio.wav"transcript = "안녕하세요"score = predict_pronunciation_score(model, processor, audio_path, transcript, device)print(f"Predicted pronunciation score: {score:.2f}")