app.py

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)