from deepspeech import Model
import gradio as gr
import numpy as np
import urllib.request

model_file_path = "deepspeech-0.9.3-models.pbmm"
lm_file_path = "deepspeech-0.9.3-models.scorer"
url = "https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/"

urllib.request.urlretrieve(url + model_file_path, filename=model_file_path)
urllib.request.urlretrieve(url + lm_file_path, filename=lm_file_path)

beam_width = 100
lm_alpha = 0.93
lm_beta = 1.18

model = Model(model_file_path)
model.enableExternalScorer(lm_file_path)
model.setScorerAlphaBeta(lm_alpha, lm_beta)
model.setBeamWidth(beam_width)


def reformat_freq(sr, y):
    if sr not in (
        48000,
        16000,
    ):  # Deepspeech only supports 16k, (we convert 48k -> 16k)
        raise ValueError("Unsupported rate", sr)
    if sr == 48000:
        y = (
            ((y / max(np.max(y), 1)) * 32767)
            .reshape((-1, 3))
            .mean(axis=1)
            .astype("int16")
        )
        sr = 16000
    return sr, y


def transcribe(audio_file):
    
    text = model.stt(audio_file)
    return text


demo = gr.Interface(
    transcribe,
    # [gr.Audio(source="microphone", streaming=True), "state"],
    gr.Audio(label="Upload Audio File", source="upload", type="filepath"),
    outputs=gr.Textbox(label="Transcript")
)

if __name__ == "__main__":
    demo.launch()