app.py

from transformers import pipeline
import gradio as gr
from pyctcdecode import BeamSearchDecoderCTC
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, AutoModel, Wav2Vec2FeatureExtractor
import librosa
import numpy as np
import subprocess

TRUST = True
SR = 16000


def resample(speech_array, sampling_rate):
    speech = torch.from_numpy(speech_array)
    print(speech, speech.shape, sampling_rate)
    resampler = torchaudio.transforms.Resample(sampling_rate)
    speech = resampler(speech).squeeze().numpy()
    return speech


def predict(speech_array, sampling_rate):
    speech = resample(speech_array, sampling_rate)
    print(speech, speech.shape)
    inputs = feature_extractor(speech, sampling_rate=SR, return_tensors="pt", padding=True)
    inputs = {key: inputs[key].to(device) for key in inputs}

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

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


config = AutoConfig.from_pretrained('Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition', trust_remote_code=TRUST)
model = AutoModel.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition", trust_remote_code=TRUST)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition")

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


def recognize(audio):
    sr, audio_array = audio
    audio_array = audio_array.astype(np.float32)
    print(sr, audio_array)
    return predict(audio_array, sr)


interface = gr.Interface(
    fn=recognize, 
    inputs=[
        gr.Audio(source="microphone", type="numpy", label="Скажите что-нибудь...")
    ],
    outputs=[
        output = gr.JSON(label="Эмоции")
    ],
    live=True)
    
interface.launch(debug=True)