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import argparse |
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import json |
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import os |
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import re |
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import tempfile |
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from pathlib import Path |
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import librosa |
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import numpy as np |
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import torch |
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from torch import no_grad, LongTensor |
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import commons |
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import utils |
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import gradio as gr |
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import gradio.utils as gr_utils |
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import gradio.processing_utils as gr_processing_utils |
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from models import SynthesizerTrn |
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from text import text_to_sequence, _clean_text |
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limitation = False |
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device = torch.device('cpu') |
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download_audio_js = """ |
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() =>{{ |
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let root = document.querySelector("body > gradio-app"); |
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if (root.shadowRoot != null) |
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root = root.shadowRoot; |
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let audio = root.querySelector("#{audio_id}").querySelector("audio"); |
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if (audio == undefined) |
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return; |
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audio = audio.src; |
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let oA = document.createElement("a"); |
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oA.download = Math.floor(Math.random()*100000000)+'.wav'; |
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oA.href = audio; |
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document.body.appendChild(oA); |
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oA.click(); |
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oA.remove(); |
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}} |
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""" |
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tts_input1 = gr.TextArea(label="inputText", value="あなたと一緒にいると、とても興奮します", elem_id=f"tts-input{0}") |
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tts_output2 = gr.Audio(label="outputAudio", elem_id=f"tts-audio{0}") |
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def get_text(text, hps, is_symbol): |
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text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = LongTensor(text_norm) |
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return text_norm |
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def create_tts_fn(model, hps, speaker_ids): |
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def tts_fn(text, speaker, speed, is_symbol): |
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if limitation: |
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text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) |
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max_len = 150 |
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if is_symbol: |
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max_len *= 3 |
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if text_len > max_len: |
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return "Error: Text is too long", None |
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speaker_id = speaker_ids[speaker] |
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stn_tst = get_text(text, hps, is_symbol) |
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with no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(device) |
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x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) |
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sid = LongTensor([speaker_id]).to(device) |
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audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, |
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length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() |
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del stn_tst, x_tst, x_tst_lengths, sid |
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return "Success", (hps.data.sampling_rate, audio) |
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return tts_fn |
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def create_to_symbol_fn(hps): |
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def to_symbol_fn(is_symbol_input, input_text, temp_text): |
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return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \ |
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else (temp_text, temp_text) |
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return to_symbol_fn |
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def main(input, authorization): |
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if os.environ['API_TOKEN'] != authorization: |
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return |
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models_tts = [] |
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models_vc = [] |
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models_soft_vc = [] |
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device = torch.device("cpu") |
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global result |
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with open("saved_model/info.json", "r", encoding="utf-8") as f: |
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models_info = json.load(f) |
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for i, info in models_info.items(): |
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if int(i) == 0: |
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name = info["title"] |
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author = info["author"] |
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lang = info["lang"] |
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example = info["example"] |
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config_path = f"saved_model/{i}/config.json" |
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model_path = f"saved_model/{i}/model.pth" |
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cover = info["cover"] |
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cover_path = f"saved_model/{i}/{cover}" if cover else None |
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hps = utils.get_hparams_from_file(config_path) |
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model = SynthesizerTrn( |
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len(hps.symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model) |
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utils.load_checkpoint(model_path, model, None) |
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model.eval().to(device) |
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speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"] |
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speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"] |
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print(speaker_ids[0]) |
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vtts = create_tts_fn(model, hps, speaker_ids) |
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symbol = create_to_symbol_fn(hps) |
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result = vtts(input, speaker_ids[0], 1, False) |
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return result[1][0], result[1][1] |
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print(models_tts) |
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demo = gr.Interface(fn=main, inputs=["text", "text"], outputs="audio") |
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if __name__ == "__main__": |
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demo.launch(debug=True) |
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