Spaces:
Sleeping
Sleeping
import soundfile as sf | |
import torch | |
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
import gradio as gr | |
import sox | |
import subprocess | |
# from google_spell_checker import GoogleSpellChecker | |
import openai | |
# Set your OpenAI API key | |
api_key = "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" | |
# spell_checker = GoogleSpellChecker(lang="fa") | |
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) | |
del(logits) | |
return transcription | |
# def corrector(sentence): | |
# check_spell = spell_checker.check(sentence) | |
# if check_spell[1] is None: | |
# return sentence | |
# else: | |
# return check_spell[1] | |
def correct_text_with_gpt(text): | |
openai.api_key = api_key | |
response = openai.Completion.create( | |
engine="text-davinci-003", | |
prompt=f"Please correct the following text: '{text}'\n\nCorrected text:", | |
max_tokens=1000, | |
temperature=0.5, # Temperature controls the randomness of the model's output. A higher value like 1.0 makes the output more random, while a lower value like 0.2 makes it more deterministic and focused. | |
top_p=1.0, # This parameter controls the diversity of the output. It sets a threshold for the cumulative probability of words to keep. Smaller values like 0.2 will result in more focused responses, while larger values like 0.8 will allow for more diversity. | |
frequency_penalty=0.2, # encourages the use of less common words | |
presence_penalty=0.5, # discourages the use of common words. | |
) | |
return response.choices[0].text.strip() | |
def parse(wav_file): | |
input_values = read_file_and_process(wav_file) | |
with torch.no_grad(): | |
logits = model(**input_values).logits | |
return correct_text_with_gpt(parse_transcription(logits)) | |
# def parse(wav_file): | |
# check_spell = '' | |
# input_values = read_file_and_process(wav_file) | |
# with torch.no_grad(): | |
# logits = model(**input_values).logits | |
# # sentence = parse_transcription(logits) | |
# check_spell = spell_checker.check(parse_transcription(logits)) | |
# # if check_spell[0] is False: | |
# # corrected = check_spell[1] | |
# # else: | |
# # corrected = sentence | |
# return spell_checker.check(parse_transcription(logits))[1] if spell_checker.check(parse_transcription(logits))[0] is False else parse_transcription(logits) | |
model_id = "jonatasgrosman/wav2vec2-large-xlsr-53-persian" | |
processor = Wav2Vec2Processor.from_pretrained(model_id) | |
model = Wav2Vec2ForCTC.from_pretrained(model_id) | |
input_ = gr.Audio(source="microphone", | |
type="filepath", | |
label="لطفا دکمه ضبط صدا را بزنید و شروع به صحبت کنید و بعذ از اتمام صحبت دوباره دکمه ضبط را فشار دهید.", | |
show_download_button=True, | |
show_edit_button=True, | |
) | |
txtbox = gr.Textbox( | |
label="متن گفتار شما: ", | |
lines=5, | |
text_align="right", | |
show_label=True, | |
show_copy_button=True, | |
) | |
title = "Speech-to-Text (persian)" | |
description = "، توجه داشته باشید که هرچه گفتار شما شمرده تر باشد خروجی با کیفیت تری دارید.روی دکمه ضبط صدا کلیک کنید و سپس دسترسی مرورگر خود را به میکروفون دستگاه بدهید، سپس شروع به صحبت کنید و برای اتمام ضبط دوباره روی دکمه کلیک کنید" | |
# article = "<p style='text-align: center'><a href='https://github.com/nimaprgrmr'>Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a></p>" | |
demo = gr.Interface(fn=parse, inputs = input_, outputs=txtbox, title=title, description=description, article = article, | |
streaming=True, interactive=True, | |
analytics_enabled=False, show_tips=False, enable_queue=True) | |
demo.launch(share=True) |