import torch import torchaudio import gradio as gr from transformers import Wav2Vec2FeatureExtractor,AutoConfig,pipeline config = AutoConfig.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1") model = Wav2Vec2FeatureExtractor.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1") def speech_file_to_array_fn(path, sampling_rate): with tempfile.NamedTemporaryFile(suffix=".wav") as temp_audio_file: # Copy the contents of the uploaded audio file to the temporary file temp_audio_file.write(open(path, "rb").read()) temp_audio_file.flush() # Load the audio file using torchaudio speech_array, _sampling_rate = torchaudio.load(temp_audio_file.name) 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 = model(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 def SER(audio): return predict(audio,model.sampling_rate) iface = gr.Interface(fn=SER, inputs="audio", outputs="text") iface.launch(share=False)