rearranged repo
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Plachta
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import os
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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 argparse
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import commons
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from mel_processing import spectrogram_torch
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import utils
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from models import SynthesizerTrn
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import gradio as gr
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import librosa
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import webbrowser
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from text import text_to_sequence, _clean_text
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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language_marks = {
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"Japanese": "",
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"日本語": "[JA]",
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"简体中文": "[ZH]",
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"English": "[EN]",
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"Mix": "",
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}
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lang = ['日本語', '简体中文', 'English', 'Mix']
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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, language, speed):
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if language is not None:
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text = language_marks[language] + text + language_marks[language]
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speaker_id = speaker_ids[speaker]
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stn_tst = get_text(text, hps, False)
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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_vc_fn(model, hps, speaker_ids):
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def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
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input_audio = record_audio if record_audio is not None else upload_audio
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if input_audio is None:
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return "You need to record or upload an audio", None
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sampling_rate, audio = input_audio
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original_speaker_id = speaker_ids[original_speaker]
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target_speaker_id = speaker_ids[target_speaker]
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
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if len(audio.shape) > 1:
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audio = librosa.to_mono(audio.transpose(1, 0))
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if sampling_rate != hps.data.sampling_rate:
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
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with no_grad():
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y = torch.FloatTensor(audio)
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y = y / max(-y.min(), y.max()) / 0.99
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y = y.to(device)
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y = y.unsqueeze(0)
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spec = spectrogram_torch(y, hps.data.filter_length,
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
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center=False).to(device)
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spec_lengths = LongTensor([spec.size(-1)]).to(device)
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sid_src = LongTensor([original_speaker_id]).to(device)
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sid_tgt = LongTensor([target_speaker_id]).to(device)
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audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
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0, 0].data.cpu().float().numpy()
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del y, spec, spec_lengths, sid_src, sid_tgt
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return "Success", (hps.data.sampling_rate, audio)
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return vc_fn
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model")
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parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file")
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parser.add_argument("--share", default=False, help="make link public (used in colab)")
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args = parser.parse_args()
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hps = utils.get_hparams_from_file(args.config_dir)
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net_g = 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).to(device)
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_ = net_g.eval()
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_ = utils.load_checkpoint(args.model_dir, net_g, None)
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speaker_ids = hps.speakers
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speakers = list(hps.speakers.keys())
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tts_fn = create_tts_fn(net_g, hps, speaker_ids)
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vc_fn = create_vc_fn(net_g, hps, speaker_ids)
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app = gr.Blocks()
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with app:
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with gr.Tab("Text-to-Speech"):
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with gr.Row():
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with gr.Column():
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textbox = gr.TextArea(label="Text",
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placeholder="Type your sentence here",
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value="こんにちわ。", elem_id=f"tts-input")
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# select character
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char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
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language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
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duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
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label='速度 Speed')
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with gr.Column():
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text_output = gr.Textbox(label="Message")
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audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
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btn = gr.Button("Generate!")
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btn.click(tts_fn,
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inputs=[textbox, char_dropdown, language_dropdown, duration_slider,],
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outputs=[text_output, audio_output])
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with gr.Tab("Voice Conversion"):
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gr.Markdown("""
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录制或上传声音，并选择要转换的音色。
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""")
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with gr.Column():
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record_audio = gr.Audio(label="record your voice", source="microphone")
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upload_audio = gr.Audio(label="or upload audio here", source="upload")
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source_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="source speaker")
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target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
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with gr.Column():
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message_box = gr.Textbox(label="Message")
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converted_audio = gr.Audio(label='converted audio')
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btn = gr.Button("Convert!")
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btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio],
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outputs=[message_box, converted_audio])
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webbrowser.open("http://127.0.0.1:7860")
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app.launch(share=args.share)
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@@ -1,151 +0,0 @@
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import os
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import argparse
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import json
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--add_auxiliary_data", type=bool, help="Whether to add extra data as fine-tuning helper")
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parser.add_argument("--languages", default="CJE")
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args = parser.parse_args()
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if args.languages == "CJE":
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langs = ["[ZH]", "[JA]", "[EN]"]
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elif args.languages == "CJ":
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langs = ["[ZH]", "[JA]"]
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elif args.languages == "C":
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langs = ["[ZH]"]
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new_annos = []
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# Source 1: transcribed short audios
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if os.path.exists("short_character_anno.txt"):
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with open("short_character_anno.txt", 'r', encoding='utf-8') as f:
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short_character_anno = f.readlines()
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new_annos += short_character_anno
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# Source 2: transcribed long audio segments
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if os.path.exists("./long_character_anno.txt"):
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with open("./long_character_anno.txt", 'r', encoding='utf-8') as f:
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long_character_anno = f.readlines()
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new_annos += long_character_anno
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# Get all speaker names
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speakers = []
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for line in new_annos:
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path, speaker, text = line.split("|")
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if speaker not in speakers:
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speakers.append(speaker)
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assert (len(speakers) != 0), "No audio file found. Please check your uploaded file structure."
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# Source 3 (Optional): sampled audios as extra training helpers
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if args.add_auxiliary_data:
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with open("./sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
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old_annos = f.readlines()
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# filter old_annos according to supported languages
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filtered_old_annos = []
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for line in old_annos:
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for lang in langs:
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if lang in line:
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filtered_old_annos.append(line)
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old_annos = filtered_old_annos
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for line in old_annos:
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path, speaker, text = line.split("|")
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if speaker not in speakers:
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speakers.append(speaker)
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num_old_voices = len(old_annos)
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num_new_voices = len(new_annos)
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# STEP 1: balance number of new & old voices
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cc_duplicate = num_old_voices // num_new_voices
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if cc_duplicate == 0:
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cc_duplicate = 1
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# STEP 2: modify config file
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with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
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hps = json.load(f)
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# assign ids to new speakers
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speaker2id = {}
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for i, speaker in enumerate(speakers):
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speaker2id[speaker] = i
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# modify n_speakers
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hps['data']["n_speakers"] = len(speakers)
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# overwrite speaker names
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hps['speakers'] = speaker2id
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hps['train']['log_interval'] = 10
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hps['train']['eval_interval'] = 100
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hps['train']['batch_size'] = 16
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hps['data']['training_files'] = "final_annotation_train.txt"
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hps['data']['validation_files'] = "final_annotation_val.txt"
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# save modified config
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with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
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json.dump(hps, f, indent=2)
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# STEP 3: clean annotations, replace speaker names with assigned speaker IDs
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import text
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cleaned_new_annos = []
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for i, line in enumerate(new_annos):
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path, speaker, txt = line.split("|")
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if len(txt) > 150:
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continue
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cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
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cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
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cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
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cleaned_old_annos = []
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for i, line in enumerate(old_annos):
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path, speaker, txt = line.split("|")
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if len(txt) > 150:
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continue
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cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
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cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
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cleaned_old_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
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# merge with old annotation
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final_annos = cleaned_old_annos + cc_duplicate * cleaned_new_annos
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# save annotation file
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with open("./final_annotation_train.txt", 'w', encoding='utf-8') as f:
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for line in final_annos:
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f.write(line)
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# save annotation file for validation
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with open("./final_annotation_val.txt", 'w', encoding='utf-8') as f:
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for line in cleaned_new_annos:
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f.write(line)
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print("finished")
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else:
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# Do not add extra helper data
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# STEP 1: modify config file
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with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
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hps = json.load(f)
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# assign ids to new speakers
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speaker2id = {}
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for i, speaker in enumerate(speakers):
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speaker2id[speaker] = i
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# modify n_speakers
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hps['data']["n_speakers"] = len(speakers)
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# overwrite speaker names
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hps['speakers'] = speaker2id
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hps['train']['log_interval'] = 10
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hps['train']['eval_interval'] = 100
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hps['train']['batch_size'] = 16
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hps['data']['training_files'] = "final_annotation_train.txt"
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hps['data']['validation_files'] = "final_annotation_val.txt"
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# save modified config
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with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
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json.dump(hps, f, indent=2)
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# STEP 2: clean annotations, replace speaker names with assigned speaker IDs
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import text
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cleaned_new_annos = []
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for i, line in enumerate(new_annos):
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path, speaker, txt = line.split("|")
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if len(txt) > 150:
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continue
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cleaned_text = text._clean_text(txt, hps['data']['text_cleaners']).replace("[ZH]", "")
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cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
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cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
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final_annos = cleaned_new_annos
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# save annotation file
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with open("./final_annotation_train.txt", 'w', encoding='utf-8') as f:
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for line in final_annos:
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f.write(line)
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# save annotation file for validation
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with open("./final_annotation_val.txt", 'w', encoding='utf-8') as f:
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for line in cleaned_new_annos:
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f.write(line)
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print("finished")
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