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import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import gradio as gr
import sox
import subprocess
import os
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(logits):
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
return transcription
def parse(wav_file):
input_values = read_file_and_process(wav_file)
with torch.no_grad():
logits = model(**input_values).logits
return parse_transcription(logits)
access_token = os.getenv("ACCESS_TOKEN")
model_id = "Anujgr8/wav2vec2-indic-hindi-codeswitch-anuj"
processor = Wav2Vec2Processor.from_pretrained(model_id,token=access_token)
model = Wav2Vec2ForCTC.from_pretrained(model_id,token=access_token)
input_ = gr.Audio(type="filepath")
txtbox = gr.Textbox(
label="Output from model will appear here:",
lines=5
)
gr.Interface(parse, inputs = [input_], outputs=txtbox).launch(inline=False); |