from io import BytesIO from typing import Tuple import wave import gradio as gr import numpy as np from pydub.audio_segment import AudioSegment import requests from os.path import exists from stt import Model from datetime import datetime MODEL_NAMES = [ "No scorer", "With scorer" ] # download model version = "v0.4" storage_url = f"https://github.com/robinhad/voice-recognition-ua/releases/download/{version}" model_name = "uk.tflite" scorer_name = "kenlm.scorer" model_link = f"{storage_url}/{model_name}" scorer_link = f"{storage_url}/{scorer_name}" def client(audio_data: np.array, sample_rate: int, use_scorer=False): output_audio = _convert_audio(audio_data, sample_rate) fin = wave.open(output_audio, 'rb') audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16) fin.close() ds = Model(model_name) if use_scorer: ds.enableExternalScorer("kenlm.scorer") result = ds.stt(audio) return result def download(url, file_name): if not exists(file_name): print(f"Downloading {file_name}") r = requests.get(url, allow_redirects=True) with open(file_name, 'wb') as file: file.write(r.content) else: print(f"Found {file_name}. Skipping download...") def stt(audio: Tuple[int, np.array], model_name: str): sample_rate, audio = audio print(f"Input sample rate: {sample_rate}. Audio file length: {round(audio.shape[0]/sample_rate ,2)}") use_scorer = True if model_name == "With scorer" else False recognized_result = client(audio, sample_rate, use_scorer) print(f"Time: {datetime.utcnow()}. Transcript: `{recognized_result}`. Scorer: {use_scorer}.") return recognized_result def _convert_audio(audio_data: np.array, sample_rate: int): source_audio = BytesIO() source_audio.write(audio_data) source_audio.seek(0) output_audio = BytesIO() wav_file: AudioSegment = AudioSegment.from_raw( source_audio, channels=1, sample_width=4, frame_rate=sample_rate ) wav_file.export(output_audio, "wav", codec="pcm_s16le", parameters=["-ar", "16k"]) output_audio.seek(0) return output_audio iface = gr.Interface( fn=stt, inputs=[ gr.inputs.Audio(type="numpy", label=None, optional=False), gr.inputs.Radio( label="Виберіть Speech-to-Text модель", choices=MODEL_NAMES, ), ], outputs=gr.outputs.Textbox(label="Output"), title="🐸🇺🇦 - Coqui STT", theme="huggingface", description="Україномовний🇺🇦 Speech-to-Text за допомогою Coqui STT", article="Якщо вам подобається, підтримайте за посиланням: [SUPPORT LINK](https://send.monobank.ua/jar/48iHq4xAXm)", ) download(model_link, model_name) download(scorer_link, scorer_name) iface.launch()