import logging import os import pathlib import time import tempfile import platform import gc if platform.system().lower() == 'windows': temp = pathlib.PosixPath pathlib.PosixPath = pathlib.WindowsPath elif platform.system().lower() == 'linux': temp = pathlib.WindowsPath pathlib.WindowsPath = pathlib.PosixPath os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" import langid langid.set_languages(['en', 'zh', 'ja']) 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 * from examples import * import gradio as gr from vocos import Vocos from transformers import WhisperProcessor, WhisperForConditionalGeneration 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_69.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, ).to(device) checkpoint = torch.load("./epoch-10.pt", map_location='cpu') missing_keys, unexpected_keys = model.load_state_dict( checkpoint["model"], strict=True ) del checkpoint 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 whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-medium") whisper = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium").to(device) whisper.config.forced_decoder_ids = None # Voice Presets preset_list = os.walk("./presets/").__next__()[2] preset_list = [preset[:-4] for preset in preset_list if preset.endswith(".npz")] 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) del path, filename, lastmodifytime, endfiletime gc.collect() except: return def transcribe_one(wav, sr): if sr != 16000: wav4trans = torchaudio.transforms.Resample(sr, 16000)(wav) else: wav4trans = wav input_features = whisper_processor(wav4trans.squeeze(0), sampling_rate=16000, return_tensors="pt").input_features # generate token ids predicted_ids = whisper.generate(input_features.to(device)) lang = whisper_processor.batch_decode(predicted_ids[:, 1])[0].strip("<|>") # decode token ids to text text_pr = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] # print the recognized text print(text_pr) if text_pr.strip(" ")[-1] not in "?!.,。，？！。、": text_pr += "." # delete all variables del wav4trans, input_features, predicted_ids gc.collect() return lang, text_pr def make_npz_prompt(name, uploaded_audio, recorded_audio, transcript_content): clear_prompts() audio_prompt = uploaded_audio if uploaded_audio is not None else recorded_audio sr, wav_pr = audio_prompt if len(wav_pr) / sr > 15: return "Rejected, Audio too long (should be less than 15 seconds)", None 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 == "": lang_pr, text_pr = transcribe_one(wav_pr, sr) lang_token = lang2token[lang_pr] text_pr = lang_token + text_pr + lang_token else: lang_pr = langid.classify(str(transcript_content))[0] lang_token = lang2token[lang_pr] transcript_content = transcript_content.replace("\n", "") 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" if lang_pr not in ['ja', 'zh', 'en']: return f"Prompt can only made with one of model-supported languages, got {lang_pr} instead", None # 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]) # delete all variables del audio_tokens, text_tokens, phonemes, lang_pr, text_pr, wav_pr, sr, uploaded_audio, recorded_audio gc.collect() return message, os.path.join(tempfile.gettempdir(), f"{name}.npz") @torch.no_grad() def infer_from_audio(text, language, accent, audio_prompt, record_audio_prompt, transcript_content): if len(text) > 150: return "Rejected, Text too long (should be less than 150 characters)", None audio_prompt = audio_prompt if audio_prompt is not None else record_audio_prompt sr, wav_pr = audio_prompt if len(wav_pr) / sr > 15: return "Rejected, Audio too long (should be less than 15 seconds)", None 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 == "": lang_pr, text_pr = transcribe_one(wav_pr, sr) lang_token = lang2token[lang_pr] text_pr = lang_token + text_pr + lang_token else: lang_pr = langid.classify(str(transcript_content))[0] text_pr = transcript_content.replace("\n", "") lang_token = lang2token[lang_pr] text_pr = lang_token + text_pr + lang_token if language == 'auto-detect': lang_token = lang2token[langid.classify(text)[0]] else: lang_token = langdropdown2token[language] lang = token2lang[lang_token] text = text.replace("\n", "") text = lang_token + text + lang_token if lang_pr not in ['ja', 'zh', 'en']: return f"Reference audio must be a speech of one of model-supported languages, got {lang_pr} instead", None # tokenize audio encoded_frames = tokenize_audio(audio_tokenizer, (wav_pr, sr)) audio_prompts = encoded_frames[0][0].transpose(2, 1).to(device) # tokenize text 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 ] ) enroll_x_lens = None if text_pr: text_prompts, _ = text_tokenizer.tokenize(text=f"{text_pr}".strip()) text_prompts, enroll_x_lens = text_collater( [ text_prompts ] ) text_tokens = torch.cat([text_prompts, text_tokens], dim=-1) text_tokens_lens += enroll_x_lens lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]] 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, ) # 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)) message = f"text prompt: {text_pr}\nsythesized text: {text}" # delete all variables del audio_prompts, text_tokens, text_prompts, phone_tokens, encoded_frames, wav_pr, sr, audio_prompt, record_audio_prompt, transcript_content gc.collect() return message, (24000, samples.squeeze(0).cpu().numpy()) @torch.no_grad() def infer_from_prompt(text, language, accent, preset_prompt, prompt_file): if len(text) > 150: return "Rejected, Text too long (should be less than 150 characters)", None clear_prompts() # text to synthesize if language == 'auto-detect': lang_token = lang2token[langid.classify(text)[0]] else: lang_token = langdropdown2token[language] lang = token2lang[lang_token] text = text.replace("\n", "") text = lang_token + text + lang_token # load prompt if prompt_file is not None: prompt_data = np.load(prompt_file.name) else: 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) 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 = 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, ) # 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)) message = f"sythesized text: {text}" # delete all variables del audio_prompts, text_tokens, text_prompts, phone_tokens, encoded_frames, prompt_file, preset_prompt gc.collect() return message, (24000, samples.squeeze(0).cpu().numpy()) 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. """ if len(text) > 1000: return "Rejected, Text too long (should be less than 1000 characters)", None mode = 'fixed-prompt' 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 = 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, ) complete_tokens = torch.cat([complete_tokens, encoded_frames.transpose(2, 1)], dim=-1) # 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)) message = f"Cut into {len(sentences)} sentences" return message, (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 = 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, ) 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 = encoded_frames.permute(2, 0, 1) features = vocos.codes_to_features(frames) samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device)) message = f"Cut into {len(sentences)} sentences" return message, (24000, samples.squeeze(0).cpu().numpy()) else: raise ValueError(f"No such mode {mode}") app = gr.Blocks() with app: gr.Markdown(top_md) with gr.Tab("Infer from audio"): gr.Markdown(infer_from_audio_md) with gr.Row(): with gr.Column(): textbox = gr.TextArea(label="Text", placeholder="Type your sentence here", value="Welcome back, Master. What can I do for you today?", elem_id=f"tts-input") language_dropdown = gr.Dropdown(choices=['auto-detect', 'English', '中文', '日本語'], value='auto-detect', label='language') accent_dropdown = gr.Dropdown(choices=['no-accent', 'English', '中文', '日本語'], value='no-accent', label='accent') textbox_transcript = gr.TextArea(label="Transcript", placeholder="Write transcript here. (leave empty to use whisper)", value="", elem_id=f"prompt-name") upload_audio_prompt = gr.Audio(label='uploaded audio prompt', source='upload', interactive=True) record_audio_prompt = gr.Audio(label='recorded audio prompt', source='microphone', interactive=True) with gr.Column(): text_output = gr.Textbox(label="Message") audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio") btn = gr.Button("Generate!") btn.click(infer_from_audio, inputs=[textbox, language_dropdown, accent_dropdown, upload_audio_prompt, record_audio_prompt, textbox_transcript], outputs=[text_output, audio_output]) textbox_mp = gr.TextArea(label="Prompt name", placeholder="Name your prompt here", value="prompt_1", elem_id=f"prompt-name") btn_mp = gr.Button("Make prompt!") prompt_output = gr.File(interactive=False) btn_mp.click(make_npz_prompt, inputs=[textbox_mp, upload_audio_prompt, record_audio_prompt, textbox_transcript], outputs=[text_output, prompt_output]) gr.Examples(examples=infer_from_audio_examples, inputs=[textbox, language_dropdown, accent_dropdown, upload_audio_prompt, record_audio_prompt, textbox_transcript], outputs=[text_output, audio_output], fn=infer_from_audio, cache_examples=False,) with gr.Tab("Make prompt"): gr.Markdown(make_prompt_md) 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") # 添加选择语言和输入台本的地方 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', source='upload', interactive=True) record_audio_prompt_2 = gr.Audio(label='recorded audio prompt', source='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]) gr.Examples(examples=make_npz_prompt_examples, inputs=[textbox2, upload_audio_prompt_2, record_audio_prompt_2, textbox_transcript2], outputs=[text_output_2, prompt_output_2], fn=make_npz_prompt, cache_examples=False,) with gr.Tab("Infer from prompt"): gr.Markdown(infer_from_prompt_md) with gr.Row(): with gr.Column(): textbox_3 = gr.TextArea(label="Text", placeholder="Type your sentence here", value="Welcome back, Master. What can I do for you today?", elem_id=f"tts-input") language_dropdown_3 = gr.Dropdown(choices=['auto-detect', 'English', '中文', '日本語', 'Mix'], value='auto-detect', label='language') accent_dropdown_3 = gr.Dropdown(choices=['no-accent', 'English', '中文', '日本語'], value='no-accent', label='accent') preset_dropdown_3 = gr.Dropdown(choices=preset_list, value=None, label='Voice preset') prompt_file = gr.File(file_count='single', file_types=['.npz'], interactive=True) with gr.Column(): text_output_3 = gr.Textbox(label="Message") audio_output_3 = gr.Audio(label="Output Audio", elem_id="tts-audio") btn_3 = gr.Button("Generate!") btn_3.click(infer_from_prompt, inputs=[textbox_3, language_dropdown_3, accent_dropdown_3, preset_dropdown_3, prompt_file], outputs=[text_output_3, audio_output_3]) gr.Examples(examples=infer_from_prompt_examples, inputs=[textbox_3, language_dropdown_3, accent_dropdown_3, preset_dropdown_3, prompt_file], outputs=[text_output_3, audio_output_3], fn=infer_from_prompt, cache_examples=False,) with gr.Tab("Infer long text"): gr.Markdown(long_text_md) 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=['auto-detect', 'English', '中文', '日本語'], value='auto-detect', label='language') accent_dropdown_4 = gr.Dropdown(choices=['no-accent', 'English', '中文', '日本語'], value='no-accent', label='accent') 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) with gr.Column(): text_output_4 = gr.TextArea(label="Message") 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=[text_output_4, audio_output_4]) app.launch()