| import torch | |
| import gradio as gr | |
| from transformers import Wav2Vec2FeatureExtractor | |
| from datasets import Dataset | |
| import librosa | |
| def get_emotion(microphone, file_upload, task): | |
| feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-er") | |
| warn_output = "" | |
| if (microphone is not None) and (file_upload is not None): | |
| warn_output = ( | |
| "WARNING: You've uploaded an audio file and used the microphone. " | |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| ) | |
| elif (microphone is None) and (file_upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| model = torch.load('model.pth', map_location=torch.device('cpu')) | |
| file = microphone if microphone is not None else file_upload | |
| speech, _ = librosa.load(file, sr=16000, mono=True) | |
| test = feature_extractor(speech, sampling_rate=16000, padding=True, return_tensors="pt" ) | |
| logits = model(**test).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()] | |
| return labels | |
| demo = gr.Blocks() | |
| mf_transcribe = gr.Interface( | |
| fn=get_emotion, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True), | |
| gr.inputs.Audio(source="upload", type="filepath", optional=True), | |
| ], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="AER", | |
| description=( | |
| "get the emotion" | |
| ), | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface([mf_transcribe],'Trancribe') | |
| demo.launch(enable_queue=True) | |