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https://github.com/bagustris/soxan

https://github.com/bagustris/soxan

Science Score: 26.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: bagustris
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Size: 36.1 KB
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  • Stars: 0
  • Watchers: 1
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Created almost 5 years ago · Last pushed over 4 years ago
Metadata Files
Readme License

README.md

Soxan

This repository consists of models, scripts, and notebooks that help you to use all the benefits of Wav2Vec 2.0 and HUBERT in your research. In the following, I'll show you how to train speech tasks in your dataset and how to use the pretrained models.

How to train

I'm just at the beginning of all the possible speech tasks. To start, we continue the training script with the speech emotion recognition problem.

Training - CMD

bash python3 run_wav2vec_clf.py \ --pooling_mode="mean" \ --model_name_or_path="lighteternal/wav2vec2-large-xlsr-53-greek" \ --model_mode="wav2vec" \ # or you can use hubert --output_dir=/path/to/output \ --cache_dir=/path/to/cache/ \ --train_file=/path/to/train.csv \ --validation_file=/path/to/dev.csv \ --test_file=/path/to/test.csv \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --gradient_accumulation_steps=2 \ --learning_rate=1e-4 \ --num_train_epochs=5.0 \ --evaluation_strategy="steps"\ --save_steps=100 \ --eval_steps=100 \ --logging_steps=100 \ --save_total_limit=2 \ --do_eval \ --do_train \ --fp16 \ --freeze_feature_extractor

Prediction

```python import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification

modelnameor_path = "path/to/your-pretrained-model"

device = torch.device("cuda" if torch.cuda.isavailable() else "cpu") config = AutoConfig.frompretrained(modelnameorpath) featureextractor = Wav2Vec2FeatureExtractor.frompretrained(modelnameorpath) samplingrate = featureextractor.sampling_rate

for wav2vec

model = Wav2Vec2ForSpeechClassification.frompretrained(modelnameorpath).to(device)

for hubert

model = HubertForSpeechClassification.frompretrained(modelnameorpath).to(device)

def speechfiletoarrayfn(path, samplingrate): speecharray, samplingrate = torchaudio.load(path) resampler = torchaudio.transforms.Resample(samplingrate, samplingrate) speech = resampler(speecharray).squeeze().numpy() return speech

def predict(path, samplingrate): speech = speechfiletoarrayfn(path, samplingrate) inputs = featureextractor(speech, samplingrate=samplingrate, returntensors="pt", padding=True) inputs = {key: inputs[key].to(device) for key in inputs}

with torch.no_grad():
    logits = model(**inputs).logits

scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in
           enumerate(scores)]
return outputs

path = "/path/to/disgust.wav" outputs = predict(path, sampling_rate)
```

Output:

bash [ {'Emotion': 'anger', 'Score': '0.0%'}, {'Emotion': 'disgust', 'Score': '99.2%'}, {'Emotion': 'fear', 'Score': '0.1%'}, {'Emotion': 'happiness', 'Score': '0.3%'}, {'Emotion': 'sadness', 'Score': '0.5%'} ]

Demos

https://huggingface.co/Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition

Owner

  • Name: Bagus Tris Atmaja
  • Login: bagustris
  • Kind: user
  • Location: Tsukuba
  • Company: AIST

Researcher @aistairc @VibrasticLab

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