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 torch from transformers import pipeline import librosa 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" ) def load_hf_model(model_path="facebook/wav2vec2-large-robust-ft-swbd-300h"): return pipeline("automatic-speech-recognition", model=model_path) # download STT model model_info = { "mixteco": ("https://coqui.gateway.scarf.sh/mixtec/jemeyer/v1.0.0/model.tflite", "mixtec.tflite"), "chatino": ("https://coqui.gateway.scarf.sh/chatino/bozden/v1.0.0/model.tflite", "chatino.tflite"), "totonaco": ("https://coqui.gateway.scarf.sh/totonac/bozden/v1.0.0/model.tflite", "totonac.tflite"), "español": ("jonatasgrosman/wav2vec2-large-xlsr-53-spanish", "spanish_xlsr"), "inglés": ("facebook/wav2vec2-large-robust-ft-swbd-300h", "english_xlsr"), } STT_MODELS = {lang: load_hf_model(model_info[lang][0]) for lang in ("español",)} def client(audio_data: np.array, sample_rate: int, default_lang: str): output_audio = _convert_audio(audio_data, sample_rate) waveform, _ = torchaudio.load(output_audio) out_prob, score, index, text_lab = lang_classifier.classify_batch(waveform) text_lab = text_lab[0] output_audio.seek(0) fin = wave.open(output_audio, 'rb') coqui_audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16) output_audio.seek(0) hf_audio, _ = librosa.load(output_audio) fin.close() print(default_lang, text_lab) if text_lab == 'Spanish': text_lab = 'español' asr_pipeline = STT_MODELS['español'] result = asr_pipeline(hf_audio, chunk_length_s=5, stride_length_s=1)['text'] else: text_lab = default_lang ds = STT_MODELS[default_lang] result = ds.stt(coqui_audio) return f"{text_lab}: {result}" def load_coqui_models(language): model_path, file_name = model_info.get(language, ("", "")) if not exists(file_name): print(f"Downloading {model_path}") r = requests.get(model_path, allow_redirects=True) with open(file_name, 'wb') as file: file.write(r.content) else: print(f"Found {file_name}. Skipping download...") return Model(file_name) for lang in ('mixteco', 'chatino', 'totonaco'): STT_MODELS[lang] = load_coqui_models(lang) def stt(default_lang: str, audio: Tuple[int, np.array]): sample_rate, audio = audio use_scorer = False recognized_result = client(audio, sample_rate, default_lang) 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.Radio(choices=("chatino", "mixteco", "totonaco"), default="mixteco", label="Lengua principal"), gr.inputs.Audio(type="numpy", label="Audio", optional=False), ], outputs=gr.outputs.Textbox(label="Output"), title="Coqui STT de Chatino, Mixteco, y Totonaco", theme="huggingface", description="Prueba de identificar frases del español en grabaciones de una lengua indígena, y prover el texto de cada una", examples=[["mixteco", "ejemplos/espanol1.wav"], ["mixteco", "ejemplos/espanol2-Yolox_BotFl_CTB501-FEF537-EGS503_40202-Acanthaceae-Ruellia_2017-01-05-h.wav"], ["mixteco", "ejemplos/mixteco1-Yolox_BotFl_CTB501-FEF537-EGS503_40202-Acanthaceae-Ruellia_2017-01-05-h.wav"], ["mixteco", "ejemplos/mixteco2-Yolox_BotFl_CTB501-FEF537-EGS503_40202-Acanthaceae-Ruellia_2017-01-05-h.wav"], ["totonaco", "ejemplos/totonaco1-Zongo_Botan_Acanthaceae-Justicia-spicigera_SLC388-IPN389_2018-07-26-i.wav"], ["totonaco", "ejemplos/totonaco2-Zongo_Botan_Acanthaceae-Justicia-spicigera_SLC388-IPN389_2018-07-26-i.wav"]], article="La identificación de lenguas usa el modelo" " [lang-id-commonlanguage-ecapa de Speechbrain](https://huggingface.co/speechbrain/lang-id-commonlanguage_ecapa)" " y aquí se supone que si la lengua no es español, debe ser la lengua principal del contexto." "\n\n" "Chatino: Prueba de dictado a texto para el chatino de la sierra (Quiahije) " " usando [el modelo entrenado por Bülent Özden](https://coqui.ai/chatino/bozden/v1.0.0)" " con [los datos recopilados por Hilaria Cruz y sys colaboradores](https://gorilla.linguistlist.org/code/ctp/)" "\n\n" "Mixteco: 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, Jonathan Amith 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" "Totonaco: Prueba de dictado a texto para el totonaco de la sierra," " usando [el modelo entrenado por Bülent Özden](https://coqui.ai/totonac/bozden/v1.0.0)" " con [los datos recopilados por Osbel López Francisco y Jonathan Amith](https://www.openslr.org/107)." " \n\n" "Los ejemplos vienen del proyecto [DEMCA](https://demca.mesolex.org/). " " Esta prueba es basada en la de [Ukraniano](https://huggingface.co/spaces/robinhad/ukrainian-stt)." ) iface.launch()