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import soundfile as sf |
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import torch |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import gradio as gr |
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import sox |
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import subprocess |
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from fuzzywuzzy import fuzz |
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from data import get_data |
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DATASET = get_data() |
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def read_file_and_process(wav_file): |
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filename = wav_file.split('.')[0] |
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filename_16k = filename + "16k.wav" |
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resampler(wav_file, filename_16k) |
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speech, _ = sf.read(filename_16k) |
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inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True) |
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return inputs |
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def resampler(input_file_path, output_file_path): |
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command = ( |
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f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn " |
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f"{output_file_path}" |
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) |
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subprocess.call(command, shell=True) |
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def parse_transcription(logits): |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) |
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return transcription |
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def parse(wav_file): |
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input_values = read_file_and_process(wav_file) |
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with torch.no_grad(): |
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logits = model(**input_values).logits |
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user_question = parse_transcription(logits) |
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return user_question |
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def get_answer(wav_file=None): |
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input_values = read_file_and_process(wav_file) |
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with torch.no_grad(): |
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logits = model(**input_values).logits |
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user_question = parse_transcription(logits) |
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highest_score = 0 |
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best_answer = None |
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for item in DATASET: |
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similarity_score = fuzz.token_set_ratio(user_question, item["question"]) |
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if similarity_score > highest_score: |
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highest_score = similarity_score |
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best_answer = item["answer"] |
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if highest_score >= 80: |
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return best_answer |
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else: |
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return "I don't have an answer to that question." |
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model_id = "jonatasgrosman/wav2vec2-large-xlsr-53-persian" |
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processor = Wav2Vec2Processor.from_pretrained(model_id) |
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model = Wav2Vec2ForCTC.from_pretrained(model_id) |
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input_ = [ |
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gr.Audio(source="microphone", |
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type="filepath", |
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label="لطفا دکمه ضبط صدا را بزنید و شروع به صحبت کنید و بعذ از اتمام صحبت دوباره دکمه ضبط را فشار دهید.", |
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show_download_button=True, |
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show_edit_button=True, |
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), |
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] |
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txtbox = gr.Textbox( |
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label="پاسخ شما: ", |
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lines=5, |
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text_align="right", |
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show_label=True, |
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show_copy_button=True, |
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) |
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title = "Speech-to-Text (persian)" |
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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" |
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article = "<p style='text-align: center'><a href='https://github.com/nimaprgrmr'>Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a></p>" |
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demo = gr.Interface(fn=get_answer, inputs = input_, outputs=txtbox, title=title, description=description, article = article, |
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streaming=True, interactive=True, |
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analytics_enabled=False, show_tips=False, enable_queue=True) |
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demo.launch(share=True) |
