import os from typing import Text import gradio as gr import soundfile as sf from transformers import pipeline import numpy as np import torch import re from speechbrain.pretrained import EncoderClassifier def create_speaker_embedding(speaker_model, waveform: np.ndarray) -> np.ndarray: with torch.no_grad(): speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform)) speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2) if device.type != 'cuda': speaker_embeddings = speaker_embeddings.squeeze().numpy() else: speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy() speaker_embeddings = torch.tensor(speaker_embeddings, dtype=dtype).unsqueeze(0).to(device) return speaker_embeddings def remove_special_characters_s(text: Text) -> Text: chars_to_remove_regex = '[\…\–\"\“\%\‘\”\�\»\«\„\`\'́]' # remove special characters text = re.sub(chars_to_remove_regex, '', text) text = re.sub("՚", "'", text) text = re.sub("’", "'", text) text = re.sub(r'ы', 'и', text) text = text.lower() return text def cyrillic_to_latin(text: Text) -> Text: replacements = [ ('а', 'a'), ('б', 'b'), ('в', 'v'), ('г', 'h'), ('д', 'd'), ('е', 'e'), ('ж', 'zh'), ('з', 'z'), ('и', 'y'), ('й', 'j'), ('к', 'k'), ('л', 'l'), ('м', 'm'), ('н', 'n'), ('о', 'o'), ('п', 'p'), ('р', 'r'), ('с', 's'), ('т', 't'), ('у', 'u'), ('ф', 'f'), ('х', 'h'), ('ц', 'ts'), ('ч', 'ch'), ('ш', 'sh'), ('щ', 'sch'), ('ь', "'"), ('ю', 'ju'), ('я', 'ja'), ('є', 'je'), ('і', 'i'), ('ї', 'ji'), ('ґ', 'g') ] for src, dst in replacements: text = text.replace(src, dst) return text device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if torch.cuda.is_available(): dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 spk_model_name = "speechbrain/spkrec-xvect-voxceleb" speaker_model = EncoderClassifier.from_hparams( source=spk_model_name, run_opts={"device": device}, savedir=os.path.join("/tmp", spk_model_name) ) waveform, samplerate = sf.read("speaker.wav") speaker_embeddings = create_speaker_embedding(speaker_model, waveform) transcriber = pipeline("text-to-speech", model="Oysiyl/speecht5_tts_common_voice_uk") def transcribe(text: Text) -> tuple((int, np.ndarray)): text = remove_special_characters_s(text) text = cyrillic_to_latin(text) out = transcriber(text, forward_params={"speaker_embeddings": speaker_embeddings}) audio, sr = out["audio"], out["sampling_rate"] return sr, audio demo = gr.Interface( transcribe, gr.Textbox(), outputs="audio", title="Text to Speech for Ukrainian language demo", description="Click on the example below or type text!", examples=[["Держава-агресор Росія закуповує комунікаційне обладнання, зокрема супутникові інтернет-термінали Starlink, для використання у війні в арабських країнах"], ["Доброго вечора, ми з України!"]], cache_examples=True ) demo.launch()