# coding: utf-8 import logging import os import pathlib import time import tempfile import platform import webbrowser import sys print(f"default encoding is {sys.getdefaultencoding()},file system encoding is {sys.getfilesystemencoding()}") print(f"You are using Python version {platform.python_version()}") if (sys.version_info[0] < 3 or sys.version_info[1] < 7): print("The Python version is too low and may cause problems") if platform.system().lower() == 'windows': temp = pathlib.PosixPath pathlib.PosixPath = pathlib.WindowsPath else: temp = pathlib.WindowsPath pathlib.WindowsPath = pathlib.PosixPath os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" import py3langid as langid langid.set_languages(['en', 'zh', 'ja', 'vi']) import nltk nltk.data.path = nltk.data.path + [os.path.join(os.getcwd(), "nltk_data")] import torch import torchaudio import numpy as np from data.tokenizer import ( AudioTokenizer, tokenize_audio, ) from data.collation import get_text_token_collater from models.vallex import VALLE from utils.g2p import PhonemeBpeTokenizer from descriptions import * from macros import * import gradio as gr import whisper from vocos import Vocos import multiprocessing thread_count = multiprocessing.cpu_count() print("Use", thread_count, "cpu cores for computing") torch.set_num_threads(thread_count) torch.set_num_interop_threads(thread_count) torch._C._jit_set_profiling_executor(False) torch._C._jit_set_profiling_mode(False) torch._C._set_graph_executor_optimize(False) text_tokenizer = PhonemeBpeTokenizer(tokenizer_path="./utils/g2p/bpe_175.json") text_collater = get_text_token_collater() device = torch.device("cpu") if torch.cuda.is_available(): device = torch.device("cuda", 0) # VALL-E-X model model = VALLE( N_DIM, NUM_HEAD, NUM_LAYERS, norm_first=True, add_prenet=False, prefix_mode=PREFIX_MODE, share_embedding=True, nar_scale_factor=1.0, prepend_bos=True, num_quantizers=NUM_QUANTIZERS, ) checkpoint = torch.load("./checkpoints/vallex-checkpoint.pt", map_location='cpu') missing_keys, unexpected_keys = model.load_state_dict( checkpoint["model"], strict=True ) assert not missing_keys model.eval() # Encodec model audio_tokenizer = AudioTokenizer(device) # Vocos decoder vocos = Vocos.from_pretrained('charactr/vocos-encodec-24khz').to(device) # ASR if not os.path.exists("./whisper/"): os.mkdir("./whisper/") try: whisper_model = whisper.load_model("medium", download_root=os.path.join(os.getcwd(), "whisper")).cpu() except Exception as e: logging.info(e) raise Exception( "\n Whisper download failed or damaged, please go to " "'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt'" "\n manually download model and put it to {} .".format(os.getcwd() + "/whisper")) # Voice Presets preset_list = os.walk("./presets/").__next__()[2] preset_list = [preset[:-4] for preset in preset_list if preset.endswith(".npz")] def inference_encoded_frames(text_tokens, text_tokens_lens, audio_prompts, enroll_x_lens, lang_pr, langs, accent, lang): if lang_pr == vi_code: lang_pr = zh_code if lang == vi_code: lang = ja_code encoded_frames = model.inference( text_tokens.to(device), text_tokens_lens.to(device), audio_prompts, enroll_x_lens=enroll_x_lens, top_k=-100, temperature=1, prompt_language=lang_pr, text_language=langs if accent == "no-accent" else lang, best_of=5, ) return encoded_frames def inference_samples(text_tokens, text_tokens_lens, audio_prompts, enroll_x_lens, lang_pr, langs, accent, lang): encoded_frames = inference_encoded_frames(text_tokens, text_tokens_lens, audio_prompts, enroll_x_lens, lang_pr, langs, accent, lang) # Decode with Vocos frames = encoded_frames.permute(2, 0, 1) features = vocos.codes_to_features(frames) samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device)) return samples def clear_prompts(): try: path = tempfile.gettempdir() for eachfile in os.listdir(path): filename = os.path.join(path, eachfile) if os.path.isfile(filename) and filename.endswith(".npz"): lastmodifytime = os.stat(filename).st_mtime endfiletime = time.time() - 60 if endfiletime > lastmodifytime: os.remove(filename) except: return def transcribe_one(model, audio_path): # load audio and pad/trim it to fit 30 seconds audio = whisper.load_audio(audio_path) audio = whisper.pad_or_trim(audio) # make log-Mel spectrogram and move to the same device as the model mel = whisper.log_mel_spectrogram(audio).to(model.device) # detect the spoken language _, probs = model.detect_language(mel) print(f"Detected language: {max(probs, key=probs.get)}") lang = max(probs, key=probs.get) # decode the audio options = whisper.DecodingOptions(temperature=1.0, best_of=5, fp16=False if device == torch.device("cpu") else True, sample_len=150) result = whisper.decode(model, mel, options) # print the recognized text print(result.text) text_pr = result.text if text_pr.strip(" ")[-1] not in "?!.,。，？！。、": text_pr += "." return lang, text_pr def make_npz_prompt(name, uploaded_audio, recorded_audio, transcript_content): global model, text_collater, text_tokenizer, audio_tokenizer clear_prompts() audio_prompt = uploaded_audio if uploaded_audio is not None else recorded_audio sr, wav_pr = audio_prompt if not isinstance(wav_pr, torch.FloatTensor): wav_pr = torch.FloatTensor(wav_pr) if wav_pr.abs().max() > 1: wav_pr /= wav_pr.abs().max() if wav_pr.size(-1) == 2: wav_pr = wav_pr[:, 0] if wav_pr.ndim == 1: wav_pr = wav_pr.unsqueeze(0) assert wav_pr.ndim and wav_pr.size(0) == 1 if transcript_content == "": text_pr, lang_pr = make_prompt(name, wav_pr, sr, save=False) else: lang_pr = langid.classify(str(transcript_content))[0] lang_token = lang2token[lang_pr] text_pr = f"{lang_token}{str(transcript_content)}{lang_token}" # tokenize audio encoded_frames = tokenize_audio(audio_tokenizer, (wav_pr, sr)) audio_tokens = encoded_frames[0][0].transpose(2, 1).cpu().numpy() # tokenize text phonemes, _ = text_tokenizer.tokenize(text=f"{text_pr}".strip()) text_tokens, enroll_x_lens = text_collater( [ phonemes ] ) message = f"Detected language: {lang_pr}\n Detected text {text_pr}\n" # save as npz file np.savez(os.path.join(tempfile.gettempdir(), f"{name}.npz"), audio_tokens=audio_tokens, text_tokens=text_tokens, lang_code=lang2code[lang_pr]) return message, os.path.join(tempfile.gettempdir(), f"{name}.npz") def make_prompt(name, wav, sr, save=True): global whisper_model whisper_model.to(device) if not isinstance(wav, torch.FloatTensor): wav = torch.tensor(wav) if wav.abs().max() > 1: wav /= wav.abs().max() if wav.size(-1) == 2: wav = wav.mean(-1, keepdim=False) if wav.ndim == 1: wav = wav.unsqueeze(0) assert wav.ndim and wav.size(0) == 1 torchaudio.save(f"./prompts/{name}.wav", wav, sr) lang, text = transcribe_one(whisper_model, f"./prompts/{name}.wav") lang_token = lang2token[lang] text = lang_token + text + lang_token with open(f"./prompts/{name}.txt", 'w', encoding='utf-8') as f: f.write(text) if not save: os.remove(f"./prompts/{name}.wav") os.remove(f"./prompts/{name}.txt") whisper_model.cpu() torch.cuda.empty_cache() return text, lang from utils.sentence_cutter import split_text_into_sentences @torch.no_grad() def infer_long_text(text, preset_prompt, prompt=None, language='auto', accent='no-accent'): """ For long audio generation, two modes are available. fixed-prompt: This mode will keep using the same prompt the user has provided, and generate audio sentence by sentence. sliding-window: This mode will use the last sentence as the prompt for the next sentence, but has some concern on speaker maintenance. """ mode = 'fixed-prompt' global model, audio_tokenizer, text_tokenizer, text_collater model.to(device) if (prompt is None or prompt == "") and preset_prompt == "": mode = 'sliding-window' # If no prompt is given, use sliding-window mode sentences = split_text_into_sentences(text) # detect language if language == "auto-detect": language = langid.classify(text)[0] else: language = token2lang[langdropdown2token[language]] # if initial prompt is given, encode it if prompt is not None and prompt != "": # load prompt prompt_data = np.load(prompt.name) audio_prompts = prompt_data['audio_tokens'] text_prompts = prompt_data['text_tokens'] lang_pr = prompt_data['lang_code'] lang_pr = code2lang[int(lang_pr)] # numpy to tensor audio_prompts = torch.tensor(audio_prompts).type(torch.int32).to(device) text_prompts = torch.tensor(text_prompts).type(torch.int32) elif preset_prompt is not None and preset_prompt != "": prompt_data = np.load(os.path.join("./presets/", f"{preset_prompt}.npz")) audio_prompts = prompt_data['audio_tokens'] text_prompts = prompt_data['text_tokens'] lang_pr = prompt_data['lang_code'] lang_pr = code2lang[int(lang_pr)] # numpy to tensor audio_prompts = torch.tensor(audio_prompts).type(torch.int32).to(device) text_prompts = torch.tensor(text_prompts).type(torch.int32) else: audio_prompts = torch.zeros([1, 0, NUM_QUANTIZERS]).type(torch.int32).to(device) text_prompts = torch.zeros([1, 0]).type(torch.int32) lang_pr = language if language != 'mix' else 'en' if mode == 'fixed-prompt': complete_tokens = torch.zeros([1, NUM_QUANTIZERS, 0]).type(torch.LongTensor).to(device) for text in sentences: text = text.replace("\n", "").strip(" ") if text == "": continue lang_token = lang2token[language] lang = token2lang[lang_token] text = lang_token + text + lang_token enroll_x_lens = text_prompts.shape[-1] logging.info(f"synthesize text: {text}") phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip()) text_tokens, text_tokens_lens = text_collater( [ phone_tokens ] ) text_tokens = torch.cat([text_prompts, text_tokens], dim=-1) text_tokens_lens += enroll_x_lens # accent control lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]] encoded_frames = inference_encoded_frames(text_tokens, text_tokens_lens, audio_prompts, enroll_x_lens, lang_pr, langs, accent, lang) complete_tokens = torch.cat([complete_tokens, encoded_frames.transpose(2, 1)], dim=-1) # Decode with Vocos frames = complete_tokens.permute(1, 0, 2) features = vocos.codes_to_features(frames) samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device)) model.to('cpu') print(f"Cut into {len(sentences)} sentences") return 24000, samples.squeeze(0).cpu().numpy() elif mode == "sliding-window": complete_tokens = torch.zeros([1, NUM_QUANTIZERS, 0]).type(torch.LongTensor).to(device) original_audio_prompts = audio_prompts original_text_prompts = text_prompts for text in sentences: text = text.replace("\n", "").strip(" ") if text == "": continue lang_token = lang2token[language] lang = token2lang[lang_token] text = lang_token + text + lang_token enroll_x_lens = text_prompts.shape[-1] logging.info(f"synthesize text: {text}") phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip()) text_tokens, text_tokens_lens = text_collater( [ phone_tokens ] ) text_tokens = torch.cat([text_prompts, text_tokens], dim=-1) text_tokens_lens += enroll_x_lens # accent control lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]] encoded_frames = inference_encoded_frames(text_tokens, text_tokens_lens, audio_prompts, enroll_x_lens, lang_pr, langs, accent, lang) complete_tokens = torch.cat([complete_tokens, encoded_frames.transpose(2, 1)], dim=-1) if torch.rand(1) < 1.0: audio_prompts = encoded_frames[:, :, -NUM_QUANTIZERS:] text_prompts = text_tokens[:, enroll_x_lens:] else: audio_prompts = original_audio_prompts text_prompts = original_text_prompts # Decode with Vocos frames = complete_tokens.permute(1, 0, 2) features = vocos.codes_to_features(frames) samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device)) model.to('cpu') return 24000, samples.squeeze(0).cpu().numpy() else: raise ValueError(f"No such mode {mode}") def main(): app = gr.Blocks(title="TTS and Voice Clone") with app: with gr.Tab("Text to Speech"): with gr.Row(): with gr.Column(): textbox_4 = gr.TextArea(label="Text", placeholder="Type your sentence here", value=long_text_example, elem_id=f"tts-input") language_dropdown_4 = gr.Dropdown(choices=language_options, value='auto-detect', label='language') accent_dropdown_4 = gr.Dropdown(choices=accent_options, value='no-accent', label='accent') with gr.Column(): preset_dropdown_4 = gr.Dropdown(choices=preset_list, value=None, label='Voice preset') prompt_file_4 = gr.File(file_count='single', file_types=['.npz'], interactive=True) audio_output_4 = gr.Audio(label="Output Audio", elem_id="tts-audio") btn_4 = gr.Button("Generate!") btn_4.click(infer_long_text, inputs=[textbox_4, preset_dropdown_4, prompt_file_4, language_dropdown_4, accent_dropdown_4], outputs=[audio_output_4]) with gr.Tab("Make prompt for voice clone"): with gr.Row(): with gr.Column(): textbox2 = gr.TextArea(label="Prompt name", placeholder="Name your prompt here", value="prompt_1", elem_id=f"prompt-name") textbox_transcript2 = gr.TextArea(label="Transcript", placeholder="Write transcript here. (leave empty to use whisper)", value="", elem_id=f"prompt-name") upload_audio_prompt_2 = gr.Audio(label='uploaded audio prompt', sources='upload', interactive=True) record_audio_prompt_2 = gr.Audio(label='recorded audio prompt', sources='microphone', interactive=True) with gr.Column(): text_output_2 = gr.Textbox(label="Message") prompt_output_2 = gr.File(interactive=False) btn_2 = gr.Button("Make!") btn_2.click(make_npz_prompt, inputs=[textbox2, upload_audio_prompt_2, record_audio_prompt_2, textbox_transcript2], outputs=[text_output_2, prompt_output_2]) webbrowser.open("http://127.0.0.1:7860") app.launch(share=True) if __name__ == "__main__": formatter = ( "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" ) logging.basicConfig(format=formatter, level=logging.INFO) main()