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| import torch | |
| import torchaudio | |
| #fixes second prediction bug | |
| torch._C._jit_override_can_fuse_on_cpu(False) | |
| torch._C._jit_override_can_fuse_on_gpu(False) | |
| torch._C._jit_set_texpr_fuser_enabled(False) | |
| torch._C._jit_set_nvfuser_enabled(False) | |
| loader = torch.jit.load("audio_loader.pt") | |
| model = torch.jit.load('QuartzNet_thunderspeech_3.pt') | |
| vocab = model.text_transform.vocab.itos | |
| vocab[-1] = '' | |
| def convert_probs(probs): | |
| ids = probs.argmax(1)[0] | |
| s = [] | |
| if vocab[ids[0]]: s.append(vocab[ids[0]]) | |
| for i in range(1,len(ids)): | |
| if ids[i-1] != ids[i]: | |
| new = vocab[ids[i]] | |
| if new: s.append(new) | |
| #return '.'.join(s) | |
| return s | |
| def predict(path): | |
| audio = loader(path) | |
| probs = model(audio, torch.tensor(audio.shape[0] * [audio.shape[-1]], device=audio.device))[0] | |
| return convert_probs(probs) | |
| gr.Interface(fn=predict, | |
| inputs=[gr.inputs.Audio(source='microphone', type='filepath', optional=True)], | |
| outputs= 'text').launch(debug=Tru |