run.py

from deepspeech import Model
import gradio as gr
import numpy as np
import urllib.request
import wave
import subprocess
import sys
import shlex
from shlex import quote

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 convert_samplerate(audio_path, desired_sample_rate):
    sox_cmd = 'sox {} --type raw --bits 16 --channels 1 --rate {} --encoding signed-integer --endian little --compression 0.0 --no-dither - '.format(quote(audio_path), desired_sample_rate)
    try:
        output = subprocess.check_output(shlex.split(sox_cmd), stderr=subprocess.PIPE)
    except subprocess.CalledProcessError as e:
        raise RuntimeError('SoX returned non-zero status: {}'.format(e.stderr))
    except OSError as e:
        raise OSError(e.errno, 'SoX not found, use {}hz files or install it: {}'.format(desired_sample_rate, e.strerror))

    return desired_sample_rate, np.frombuffer(output, np.int16)



def transcribe(audio_file):
    desired_sample_rate = model.sampleRate()
    fin = wave.open(audio_file, 'rb')
    fs_orig = fin.getframerate()
    if fs_orig != desired_sample_rate:
        print('Warning: original sample rate ({}) is different than {}hz. Resampling might produce erratic speech recognition.'.format(fs_orig, desired_sample_rate), file=sys.stderr)
        fs_new, audio = convert_samplerate(audio_file, desired_sample_rate)
    else:
        audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)

    audio_length = fin.getnframes() * (1/fs_orig)
    fin.close()
    
    text = model.stt(audio)
    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()