HuBERT-SER

This repository consists of models, scripts, and notebooks that help you to use all the benefits of HuBERT 2.0 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.

Training - CMD

python "HuBERT-SER\run_wav2vec_clf.py" --pooling_mode="mean" --model_name_or_path="facebook/hubert-large-ll60k" --model_mode="hubert" --output_dir="path\to\output" --cache_dir="path\to\cache" --train_file="dataset\train.csv" --validation_file="dataset\eval.csv" --test_file="dataset\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=9.0 --evaluation_strategy='steps' --save_steps=100 --eval_steps=100 --logging_steps=100 --save_total_limit=2 --do_eval --do_train --freeze_feature_extractor

Prediction

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

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

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate

model = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device)

def speech_file_to_array_fn(path, sampling_rate):
    speech_array, _sampling_rate = torchaudio.load(path)
    resampler = torchaudio.transforms.Resample(_sampling_rate, sampling_rate)
    speech = resampler(speech_array).squeeze().numpy()
    return speech


def predict(path, sampling_rate):
    speech = speech_file_to_array_fn(path, sampling_rate)
    inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="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 = "./dataset/disgust.wav"
outputs = predict(path, sampling_rate)
print(outputs)

Output:

[
    {'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%'}
]