| import gradio as gr | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torchaudio | |
| from transformers import AutoConfig, Wav2Vec2FeatureExtractor | |
| 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/wav2vec2-base-100k-voxpopuli-gtzan-music" | |
| config = AutoConfig.from_pretrained(model_name_or_path) | |
| feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) | |
| sampling_rate = feature_extractor.sampling_rate | |
| model = Wav2Vec2ForSpeechClassification.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 = "La Campanella.mp3" | |
| outputs = predict(path, sampling_rate) | |
| iface = gr.Interface(fn=predict, inputs=path, outputs=predict(path, sampling_rate)) | |
| iface.launch() | |