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| 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() | |