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import os |
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import time |
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from datetime import datetime |
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import gradio as gr |
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import torchaudio |
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from model import ( |
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get_gigaspeech_pre_trained_model, |
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sample_rate, |
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get_wenetspeech_pre_trained_model, |
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) |
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models = { |
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"Chinese": get_wenetspeech_pre_trained_model(), |
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"English": get_gigaspeech_pre_trained_model(), |
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} |
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def convert_to_wav(in_filename: str) -> str: |
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"""Convert the input audio file to a wave file""" |
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out_filename = in_filename + ".wav" |
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print(f"Converting '{in_filename}' to '{out_filename}'") |
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_ = os.system(f"ffmpeg -hide_banner -i '{in_filename}' '{out_filename}'") |
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return out_filename |
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demo = gr.Blocks() |
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def process(in_filename: str, language: str) -> str: |
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print("in_filename", in_filename) |
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print("language", language) |
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filename = convert_to_wav(in_filename) |
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now = datetime.now() |
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date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f") |
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print(f"Started at {date_time}") |
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start = time.time() |
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wave, wave_sample_rate = torchaudio.load(filename) |
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if wave_sample_rate != sample_rate: |
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print( |
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f"Expected sample rate: {sample_rate}. Given: {wave_sample_rate}. " |
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f"Resampling to {sample_rate}." |
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) |
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wave = torchaudio.functional.resample( |
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wave, |
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orig_freq=wave_sample_rate, |
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new_freq=sample_rate, |
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) |
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wave = wave[0] |
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hyp = models[language].decode_waves([wave])[0] |
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date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f") |
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end = time.time() |
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duration = wave.shape[0] / sample_rate |
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rtf = (end - start) / duration |
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print(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s") |
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print(f"Duration {duration: .3f} s") |
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print(f"RTF {rtf: .3f}") |
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print("hyp") |
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print(hyp) |
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return hyp |
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title = "# Automatic Speech Recognition with Next-gen Kaldi" |
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description = """ |
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This space shows how to do automatic speech recognition with Next-gen Kaldi. |
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See more information by visiting the following links: |
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- <https://github.com/k2-fsa/icefall> |
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- <https://github.com/k2-fsa/sherpa> |
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- <https://github.com/k2-fsa/k2> |
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- <https://github.com/lhotse-speech/lhotse> |
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""" |
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with demo: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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language_choices = list(models.keys()) |
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language = gr.inputs.Radio( |
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label="Language", |
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choices=language_choices, |
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) |
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with gr.Tabs(): |
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with gr.TabItem("Upload from disk"): |
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uploaded_file = gr.inputs.Audio( |
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source="upload", |
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type="filepath", |
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optional=False, |
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label="Upload from disk", |
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) |
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upload_button = gr.Button("Submit for recognition") |
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uploaded_output = gr.outputs.Textbox( |
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label="Recognized speech from uploaded file" |
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) |
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with gr.TabItem("Record from microphone"): |
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microphone = gr.inputs.Audio( |
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source="microphone", |
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type="filepath", |
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optional=False, |
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label="Record from microphone", |
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) |
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recorded_output = gr.outputs.Textbox( |
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label="Recognized speech from recordings" |
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) |
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record_button = gr.Button("Submit for recordings") |
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upload_button.click( |
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process, |
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inputs=[uploaded_file, language], |
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outputs=uploaded_output, |
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) |
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record_button.click( |
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process, |
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inputs=[microphone, language], |
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outputs=recorded_output, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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