app.py

# import gradio as gr

# gr.Interface.load("models/rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset").launch()


import gradio as gr
import os
import transformers
from transformers import pipeline, Wav2Vec2ForCTC,Wav2Vec2Processor
import time
import torch

# def greet_from_secret(ignored_param):
#     name = os.environ.get('TOKEN')
#     return 


auth_token = os.environ.get('TOKEN')


M1 = "rohitp1/dgx1_w2v2_base_teacher_student_distillation_mozilla_epochs_100_batch_16_concatenate_datasets"
M2 = "rohitp1/finetune_teacher_babble_noise_mozilla_200_epochs"
M3 = "rohitp1/finetune_teacher_clean_mozilla_200_epochs"

model1 = Wav2Vec2ForCTC.from_pretrained(M1, use_auth_token=auth_token)
processor1 = Wav2Vec2Processor.from_pretrained(M1, use_auth_token=auth_token)


model2 = Wav2Vec2ForCTC.from_pretrained(M2, use_auth_token=auth_token)
processor2 = Wav2Vec2Processor.from_pretrained(M2, use_auth_token=auth_token)


model3 = Wav2Vec2ForCTC.from_pretrained(M3, use_auth_token=auth_token)
processor3 = Wav2Vec2Processor.from_pretrained(M3, use_auth_token=auth_token)



# make quantized model
quantized_model1 = torch.quantization.quantize_dynamic(
    model3, {torch.nn.Linear}, dtype=torch.qint8
)


p1 = pipeline('automatic-speech-recognition', model=model1, processor=processor1)
p2 = pipeline('automatic-speech-recognition', model=model2, processor=processor2)
p3 = pipeline('automatic-speech-recognition', model=model3, processor=processor3)
p1_quant = pipeline('automatic-speech-recognition', model=quantized_model1, processor=processor1)

def transcribe(mic_input, upl_input, model_type):
    if mic_input:
        audio = mic_input
    else:
        audio = upl_input
    time.sleep(3)
    st_time = time.time()
    if model_type == 'NoisyFinetuned':
        text = p2(audio)["text"]
    elif model_type == 'CleanFinetuned':
        text = p3(audio)["text"]
    elif model_type == 'DistilledQuantised':
        text = p1_quant(audio)['text']
    else:
        text = p1(audio)["text"]
    end_time = time.time()
    # state = text + " "
    time_taken = round((end_time - st_time) / 60 , 4)
    return text, time_taken



# gr.Interface(
#     fn=transcribe, 
#     inputs=[
#         gr.inputs.Audio(source="microphone", type="filepath"),
#         'state'
#     ],
#     outputs=[
#         "textbox",
#         "state"
#     ],
#     live=False).launch()


# demo = gr.load(
#     "huggingface/rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset",
#     title="Speech-to-text",
#     inputs="mic",
#     description="Let me try to guess what you're saying!",
#     api_key="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"
# )

# demo.launch()

def clear_inputs_and_outputs():
    return [None, None, "CleanFinetuned", None, None]

# Main function
if __name__ == "__main__":
    demo = gr.Blocks()

    with demo:
        gr.Markdown(
            """
            <center><h1> Noise Robust English Automatic Speech Recognition LibriSpeech Dataset</h1></center> \
            This space is a demo of an English ASR model using Huggingface.<br> \
    In this space, you can record your voice or upload a wav file and the model will predict the text spoken in the audio<br><br>
            """
        )
        with gr.Row():
            ## Input
            with gr.Column():
                mic_input = gr.Audio(source="microphone", type="filepath", label="Record your own voice")
                upl_input = gr.Audio(
                    source="upload", type="filepath", label="Upload a wav file"
                )

                with gr.Row():
                    model_type = gr.inputs.Dropdown(["RobustDistillation", "NoisyFinetuned", "CleanFinetuned", "DistilledAndQuantised"], label='Model Type')

                with gr.Row():
                    clr_btn = gr.Button(value="Clear", variant="secondary")
                    prd_btn = gr.Button(value="Predict")


            # Outputs
            with gr.Column():
                lbl_output = gr.Label(label="Transcription")

                with gr.Row():
                    time_output = gr.Label(label="Time Taken (in sec)")
                    # with gr.Group():
                    #     gr.Markdown("<center>Prediction per time slot</center>")
                    #     plt_output = gr.Plot(
                    #         label="Prediction per time slot", show_label=False
                    #     )


        with gr.Row():
            gr.Examples(
            [ 
                # os.path.join(os.path.dirname(__file__), "audio/sample1.wav"),
                # os.path.join(os.path.dirname(__file__), "audio/sample2.wav"),
                os.path.join(os.path.dirname(__file__), "audio/sample3.wav"),
            ],
            upl_input,
            [lbl_output, time_output],
            transcribe
        )
        # Credits
        with gr.Row():
            gr.Markdown(
                """
                <h4>Credits</h4>
                Author:  Rohit Prasad <br>
                Check out the model <a href="https://huggingface.co/rohitp1/subh_whisper_small_distil_att_loss_mozilla_epochs_50_batch_8">here</a>
                """
            )

        clr_btn.click(
            fn=clear_inputs_and_outputs,
            inputs=[],
            outputs=[mic_input, upl_input, model_type, lbl_output, time_output],
        )
        prd_btn.click(
            fn=transcribe,
            inputs=[mic_input, upl_input, model_type],
            outputs=[lbl_output, time_output],
        )

    demo.launch(debug=True)