import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import gradio as gr import scipy.signal as sps import sox def convert(inputfile, outfile): sox_tfm = sox.Transformer() sox_tfm.set_output_format( file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16 ) #print(this is not done) sox_tfm.build(inputfile, outfile) def read_file(wav): sample_rate, signal = wav signal = signal.mean(-1) number_of_samples = round(len(signal) * float(16000) / sample_rate) resampled_signal = sps.resample(signal, number_of_samples) return resampled_signal def parse_transcription(wav_file): ''' filename = wav_file.name.split('.')[0] convert(wav_file.name, filename + "16k.wav") speech, _ = sf.read(filename + "16k.wav") ''' speech = read_file(wav_file) input_values = processor(speech, sampling_rate=16_000, return_tensors="pt").input_values logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) return transcription processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") #input_ = gr.inputs.Audio(source="microphone", type="file") input_ = gr.inputs.Audio(source="microphone", type="numpy") gr.Interface(parse_transcription, inputs = input_, outputs="text", analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);