|
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 = "aditii09/hc_hindi_asr" |
|
|
|
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); |