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import os |
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from typing import Text |
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import gradio as gr |
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import soundfile as sf |
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from transformers import pipeline |
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import numpy as np |
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import torch |
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import re |
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from speechbrain.pretrained import EncoderClassifier |
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def create_speaker_embedding(speaker_model, waveform: np.ndarray) -> np.ndarray: |
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with torch.no_grad(): |
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speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform)) |
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speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2) |
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if device.type != 'cuda': |
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speaker_embeddings = speaker_embeddings.squeeze().numpy() |
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else: |
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speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy() |
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speaker_embeddings = torch.tensor(speaker_embeddings, dtype=dtype).unsqueeze(0).to(device) |
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return speaker_embeddings |
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def remove_special_characters_s(text: Text) -> Text: |
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chars_to_remove_regex = '[\-\…\–\"\“\%\‘\”\�\»\«\„\`\'́]' |
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text = re.sub(chars_to_remove_regex, '', text) |
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text = re.sub("՚", "'", text) |
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text = re.sub("’", "'", text) |
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text = re.sub(r'ы', 'и', text) |
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text = text.lower() |
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return text |
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def cyrillic_to_latin(text: Text) -> Text: |
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replacements = [ |
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('а', 'a'), |
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('б', 'b'), |
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('в', 'v'), |
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('г', 'h'), |
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('д', 'd'), |
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('е', 'e'), |
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('ж', 'zh'), |
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('з', 'z'), |
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('и', 'y'), |
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('й', 'j'), |
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('к', 'k'), |
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('л', 'l'), |
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('м', 'm'), |
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('н', 'n'), |
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('о', 'o'), |
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('п', 'p'), |
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('р', 'r'), |
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('с', 's'), |
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('т', 't'), |
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('у', 'u'), |
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('ф', 'f'), |
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('х', 'h'), |
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('ц', 'ts'), |
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('ч', 'ch'), |
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('ш', 'sh'), |
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('щ', 'sch'), |
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('ь', "'"), |
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('ю', 'ju'), |
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('я', 'ja'), |
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('є', 'je'), |
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('і', 'i'), |
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('ї', 'ji'), |
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('ґ', 'g') |
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] |
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for src, dst in replacements: |
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text = text.replace(src, dst) |
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return text |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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if torch.cuda.is_available(): |
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dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 |
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else: |
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dtype = torch.float32 |
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spk_model_name = "speechbrain/spkrec-xvect-voxceleb" |
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speaker_model = EncoderClassifier.from_hparams( |
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source=spk_model_name, |
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run_opts={"device": device}, |
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savedir=os.path.join("/tmp", spk_model_name) |
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) |
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waveform, samplerate = sf.read("files/speaker.wav") |
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speaker_embeddings = create_speaker_embedding(speaker_model, waveform) |
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transcriber = pipeline("text-to-speech", model="Oysiyl/speecht5_tts_common_voice_uk") |
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def transcribe(text: Text) -> tuple((int, np.ndarray)): |
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text = remove_special_characters_s(text) |
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text = cyrillic_to_latin(text) |
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out = transcriber(text, forward_params={"speaker_embeddings": speaker_embeddings}) |
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audio, sr = out["audio"], out["sampling_rate"] |
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return sr, audio |
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demo = gr.Interface( |
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transcribe, |
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gr.Textbox(), |
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outputs="audio", |
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title="Text to Speech for Ukrainian language demo", |
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description="Click on the example below or type text!", |
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examples=[["Держава-агресор Росія закуповує комунікаційне обладнання, зокрема супутникові інтернет-термінали Starlink, для використання у війні в арабських країнах"], |
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["Доброго вечора, ми з України!"]], |
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cache_examples=True |
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) |
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demo.launch() |