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import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import gradio as gr
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
)
sox_tfm.build(inputfile, outfile)
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
def parse_transcription(wav_file):
filename = wav_file.name.split('.')[0]
convert(wav_file.name, filename + "16k.wav")
speech, _ = sf.read(filename + "16k.wav")
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,
output1 = gr.outputs.Textbox(label="Transcription in English: ")
output2 = gr.outputs.Textbox(label="Validated Transcription in English")
input_ = gr.inputs.Audio(source="microphone", type="file")
#gr.Interface(parse_transcription, inputs = input_, outputs="text",
# analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);
gr.Interface(parse_transcription, inputs = input_, outputs=[output1, output2], analytics_enabled=False,
show_tips=False,
theme='huggingface',
layout='vertical',
title="Piecurus Test on Speech Transcription",
description="This is a live demo for Speech to Text Translation. Models used: facebook/wav2vec2-base-960h", enable_queue=True).launch( inline=False)
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