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import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
import gradio as gr
import sox
import subprocess
def read_file_and_process(wav_file):
filename = wav_file.split('.')[0]
filename_16k = filename + "16k.wav"
resampler(wav_file, filename_16k)
speech, _ = sf.read(filename_16k)
inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
return inputs
def resampler(input_file_path, output_file_path):
command = (
f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
f"{output_file_path}"
)
subprocess.call(command, shell=True)
def parse_transcription_with_lm(logits):
result = processor_with_LM.batch_decode(logits.cpu().numpy())
text = result.text
transcription = text[0].replace('<s>','')
return transcription
def parse_transcription(logits):
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
return transcription
def parse(wav_file, applyLM):
input_values = read_file_and_process(wav_file)
with torch.no_grad():
logits = model(**input_values).logits
if applyLM:
return parse_transcription_with_lm(logits)
else:
return parse_transcription(logits)
model_id = "Harveenchadha/vakyansh-wav2vec2-hindi-him-4200"
processor = Wav2Vec2Processor.from_pretrained(model_id)
processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)
input_ = gr.Audio(source="microphone", type="filepath")
txtbox = gr.Textbox(
label="Output from model will appear here:",
lines=5
)
chkbox = gr.Checkbox(label="Apply LM", value=False)
gr.Interface(parse, inputs = [input_, chkbox], outputs=txtbox,
streaming=True, interactive=True,
analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False); |