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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 | |
| import torchaudio | |
| from speechbrain.pretrained import EncoderClassifier | |
| # initialize language ID model | |
| lang_classifier = EncoderClassifier.from_hparams(source="speechbrain/lang-id-commonlanguage_ecapa", savedir="pretrained_models/lang-id-commonlanguage_ecapa") | |
| # download STT model | |
| storage_url = "https://coqui.gateway.scarf.sh/mixtec/jemeyer/v1.0.0" | |
| model_name = "model.tflite" | |
| model_link = f"{storage_url}/{model_name}" | |
| def client(audio_data: np.array, sample_rate: int, use_scorer=False): | |
| output_audio = _convert_audio(audio_data, sample_rate) | |
| out_prob, score, index, text_lab = lang_classifier.classify_file(output_audio) | |
| 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 f"{text_lab}: {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]): | |
| sample_rate, audio = audio | |
| use_scorer = False | |
| recognized_result = client(audio, sample_rate, 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.from_raw( | |
| source_audio, | |
| channels=1, | |
| sample_width=2, | |
| frame_rate=sample_rate | |
| ) | |
| wav_file.set_frame_rate(16000).set_channels( | |
| 1).export(output_audio, "wav", codec="pcm_s16le") | |
| output_audio.seek(0) | |
| return output_audio | |
| iface = gr.Interface( | |
| fn=stt, | |
| inputs=[ | |
| gr.inputs.Audio(type="numpy", | |
| label=None, optional=False), | |
| ], | |
| outputs=gr.outputs.Textbox(label="Output"), | |
| title="Coqui STT Yoloxochitl Mixtec", | |
| theme="huggingface", | |
| description="Prueba de dictado a texto para el mixteco de Yoloxochitl," | |
| " usando [el modelo entrenado por Josh Meyer](https://coqui.ai/mixtec/jemeyer/v1.0.0/)" | |
| " con [los datos recopilados por Rey Castillo y sus colaboradores](https://www.openslr.org/89)." | |
| " Esta prueba es basada en la de [Ukraniano](https://huggingface.co./spaces/robinhad/ukrainian-stt)." | |
| " \n\n" | |
| "Speech-to-text demo for Yoloxochitl Mixtec," | |
| " using [the model trained by Josh Meyer](https://coqui.ai/mixtec/jemeyer/v1.0.0/)" | |
| " on [the corpus compiled by Rey Castillo and collaborators](https://www.openslr.org/89)." | |
| " This demo is based on the [Ukrainian STT demo](https://huggingface.co./spaces/robinhad/ukrainian-stt).", | |
| ) | |
| download(model_link, model_name) | |
| iface.launch() | |