Whisper Large V3 Speech Flow

🚀 Whisper Large v3 for Speech Flow (Fluency) Classification

This model is designed for speech fluency classification, offering a solution to accurately assess speech flow and identify disfluency types.

🚀 Quick Start

✨ Features

• This model includes the implementation of speech fluency classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits.
• The model first predicts the speech with a 3 - second window size and 1 - second step size in ["fluent", "disfluent"].
• If disfluent speech is detected, it predicts the disfluent types in ["Block", "Prolongation", "Sound Repetition", "Word Repetition", "Interjection"].

📦 Installation

Download repo

git clone git@github.com:tiantiaf0627/vox-profile-release.git

Install the package

conda create -n vox_profile python=3.8
cd vox-profile-release
pip install -e .

💻 Usage Examples

Basic Usage

# Load libraries
import torch
import torch.nn.functional as F
from src.model.fluency.whisper_fluency import WhisperWrapper

# Find device
device = torch.device("cuda") if torch.cuda.is_available() else "cpu"

# Load model from Huggingface
model = WhisperWrapper.from_pretrained("tiantiaf/whisper-large-v3-speech-flow").to(device)
model.eval()

Advanced Usage

# Prediction
audio_data = torch.zeros([1, 16000*10]).float().to(device)
audio_segment = (audio_data.shape[1] - 3*16000) // 16000 + 1
if audio_segment < 1: audio_segment = 1
input_audio = list()
input_audio_length = list()
for idx in range(audio_segment): 
    input_audio.append(audio_data[0, 16000*idx:16000*idx+3*16000])
    input_audio_length.append(torch.tensor(len(audio_data[0, 16000*idx:16000*idx+3*16000])))
input_audio = torch.stack(input_audio, dim=0)
input_audio_length = torch.stack(input_audio_length, dim=0)

# Prediction
fluency_outputs, disfluency_type_outputs = model(input_audio, length=input_audio_length)
fluency_prob   = F.softmax(fluency_outputs, dim=1).detach().cpu().numpy().astype(float).tolist()

disfluency_type_prob = nn.Sigmoid()(disfluency_type_outputs)
# we can set a higher threshold in practice
disfluency_type_predictions = (disfluency_type_prob > 0.7).int().detach().cpu().numpy().tolist()
disfluency_type_prob = disfluency_type_prob.cpu().numpy().astype(float).tolist()

# Now let's gather the predictions for the utterance
utterance_fluency_list = list()
utterance_disfluency_list = list()
for audio_idx in range(audio_segment):
  disfluency_type = list()
  if fluency_prob[audio_idx][0] > 0.5: 
      utterance_fluency_list.append("fluent")
  else: 
      # If the prediction is disfluent, then which disfluency type
      utterance_fluency_list.append("disfluent")
      predictions = disfluency_type_predictions[audio_idx]
      for label_idx in range(len(predictions)):
          if predictions[label_idx] == 1:
            disfluency_type.append(disfluency_type_labels[label_idx])
  utterance_disfluency_list.append(disfluency_type)

# Now print how fluent is the utterance
print(utterance_fluency_list)
print(utterance_disfluency_list)

📚 Documentation

If you have any questions, please contact: Tiantian Feng (tiantiaf@usc.edu)

📄 License

This model is licensed under the Apache 2.0 license.

📖 Citation

Kindly cite our paper if you are using our model or find it useful in your work

@article{feng2025vox,
  title={Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits},
  author={Feng, Tiantian and Lee, Jihwan and Xu, Anfeng and Lee, Yoonjeong and Lertpetchpun, Thanathai and Shi, Xuan and Wang, Helin and Thebaud, Thomas and Moro-Velazquez, Laureano and Byrd, Dani and others},
  journal={arXiv preprint arXiv:2505.14648},
  year={2025}
}