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import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
import gradio as gr
import scipy.signal as sps
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
)
#print(this is not done)
sox_tfm.build(inputfile, outfile)
def read_file(wav):
sample_rate, signal = wav
signal = signal.mean(-1)
number_of_samples = round(len(signal) * float(16000) / sample_rate)
resampled_signal = sps.resample(signal, number_of_samples)
return resampled_signal
def parse_transcription_with_lm(wav_file):
speech = convert_file(wav_file)
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(**inputs).logits
int_result = processor.batch_decode(logits.cpu().numpy())
transcription = int_result.text
return transcription
def convert_file(wav_file):
filename = wav_file.split('.')[0]
convert(wav_file, filename + "16k.wav")
speech, _ = sf.read(filename + "16k.wav")
return speech
def parse(wav_file, applyLM):
if applyLM:
return parse_transcription_with_lm(wav_file)
else:
return parse_transcription(wav_file)
def parse_transcription(wav_file):
speech = convert_file(wav_file)
#speech = read_file(wav_file)
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
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")
#input_ = gr.inputs.Audio(source="microphone", type="numpy")
txtbox = gr.Textbox(
label="Output from model will appear here:",
lines=5
)
chkbox = gr.Checkbox(label="Apply LM", value=False)
gr.Interface(parse_transcription, inputs = [input_, chkbox], outputs=txtbox,
streaming=True, interactive=True,
analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False); |