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import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
from glob import glob
import subprocess
import gradio as gr
def get_filename(wav_file):
filename_local = wav_file.split('/')[-1][:-4]
filename_new = '/tmp/'+filename_local+'_16.wav'
subprocess.call(["sox {} -r {} -b 16 -c 1 {}".format(wav_file, str(16000), filename_new)], shell=True)
return filename_new
def parse_transcription(wav_file):
# load pretrained model
# load audio
wav_file = get_filename(wav_file.name)
audio_input, sample_rate = sf.read(wav_file)
#test_file = resampler(test_file[0])
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=16_000, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
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")
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); |