Emotion Recognition in Persian (fa) Speech using HuBERT
How to use
Requirements
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!git clone https://github.com/m3hrdadfi/soxan.git .
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
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = "m3hrdadfi/hubert-base-persian-speech-gender-recognition"
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)
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 = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs
path = "/path/to/female.wav"
outputs = predict(path, sampling_rate)
[{'Label': 'F', 'Score': '98.2%'}, {'Label': 'M', 'Score': '1.8%'}]
Evaluation
The following tables summarize the scores obtained by model overall and per each class.
Emotions | precision | recall | f1-score | accuracy |
---|---|---|---|---|
F | 0.98 | 0.97 | 0.98 | |
M | 0.98 | 0.99 | 0.98 | |
Overal | 0.98 |
Questions?
Post a Github issue from HERE.
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