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import torch | |
from transformers import SpeechEncoderDecoder, Wav2Vec2Processor | |
import gradio as gr | |
import scipy.signal as sps | |
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): | |
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("facebook/wav2vec2-xls-r-300m-en-to-15", use_auth_token="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") | |
model = SpeechEncoderDecoder.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15", use_auth_token="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") | |
#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); |