rendering/universal_tts/models/cosyvoice/cli/frontend.py

# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Generator
import json
import onnxruntime
import torch
import numpy as np
import whisper
from typing import Callable
import torchaudio.compliance.kaldi as kaldi
import os
import re
import inflect
from models.cosyvoice.utils.file_utils import logging, load_wav
from models.cosyvoice.utils.frontend_utils import (
    contains_chinese,
    replace_blank,
    replace_corner_mark,
    remove_bracket,
    spell_out_number,
    split_paragraph,
    is_only_punctuation,
)
from models.cosyvoice.tokenizer.tokenizer import get_qwen_tokenizer
from models.matcha_tts.utils.audio import mel_spectrogram


class CosyVoiceFrontEnd:

    def __init__(
        self,
        campplus_model: str,
        speech_tokenizer_model: str,
        spk2info: str = "",
        allowed_special: str = "all",
        tokenizer_path: str = "",
        skip_special_tokens: bool = True,
        mel_config={
            "n_fft": 1920,
            "num_mels": 80,
            "sampling_rate": 24000,
            "hop_size": 480,
            "win_size": 1920,
            "fmin": 0,
            "fmax": 8000,
            "center": False,
        },
    ):
        self.mel_config = mel_config
        self.tokenizer = get_qwen_tokenizer(tokenizer_path, skip_special_tokens)
        self.feat_extractor = mel_spectrogram
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        option = onnxruntime.SessionOptions()
        option.graph_optimization_level = (
            onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
        )
        option.intra_op_num_threads = 1
        self.campplus_session = onnxruntime.InferenceSession(
            campplus_model, sess_options=option, providers=["CPUExecutionProvider"]
        )
        self.speech_tokenizer_session = onnxruntime.InferenceSession(
            speech_tokenizer_model,
            sess_options=option,
            providers=[
                (
                    "CUDAExecutionProvider"
                    if torch.cuda.is_available()
                    else "CPUExecutionProvider"
                )
            ],
        )
        if os.path.exists(spk2info):
            self.spk2info = torch.load(
                spk2info, map_location=self.device, weights_only=True
            )
        else:
            self.spk2info = {}
        self.allowed_special = allowed_special
        self.inflect_parser = inflect.engine()
        # NOTE compatible when no text frontend tool is avaliable
        try:
            import ttsfrd

            self.frd = ttsfrd.TtsFrontendEngine()
            ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
            assert (
                self.frd.initialize(
                    "{}/../../pretrained_models/CosyVoice-ttsfrd/resource".format(
                        ROOT_DIR
                    )
                )
                is True
            ), "failed to initialize ttsfrd resource"
            self.frd.set_lang_type("pinyinvg")
            self.text_frontend = "ttsfrd"
            logging.info("use ttsfrd frontend")
        except:
            try:
                from wetext import Normalizer as ZhNormalizer
                from wetext import Normalizer as EnNormalizer

                self.zh_tn_model = ZhNormalizer(remove_erhua=False)
                self.en_tn_model = EnNormalizer()
                self.text_frontend = "wetext"
                logging.info("use wetext frontend")
            except:
                self.text_frontend = ""
                logging.info("no frontend is avaliable")

    def _extract_text_token(self, text):
        if isinstance(text, Generator):
            logging.info(
                "get tts_text generator, will return _extract_text_token_generator!"
            )
            # NOTE add a dummy text_token_len for compatibility
            return self._extract_text_token_generator(text), torch.tensor(
                [0], dtype=torch.int32
            ).to(self.device)
        else:
            text_token = self.tokenizer.encode(
                text, allowed_special=self.allowed_special
            )
            text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device)
            text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(
                self.device
            )
            return text_token, text_token_len

    def _extract_text_token_generator(self, text_generator):
        for text in text_generator:
            text_token, _ = self._extract_text_token(text)
            for i in range(text_token.shape[1]):
                yield text_token[:, i : i + 1]

    def _extract_speech_token(self, prompt_wav):
        speech = load_wav(prompt_wav, 16000)
        if speech.size(1) > 16000 * 30:
            speech = torch.split(
                speech, speech.size(1) // (speech.size(1) // (16000 * 20) + 1), dim=1
            )
            temp_tokens = []
            speech_token_len = 0
            for item in speech:
                if item.size(1) > 16000 * 0.5:
                    speech_token, temp_token_len = self._speech_2_speechtoken(item)
                    speech_token = speech_token.detach().cpu()
                    temp_tokens.append(speech_token)
                    speech_token_len += temp_token_len
            speech_token = torch.cat(temp_tokens, -1)
            #
        else:
            speech_token, speech_token_len = self._speech_2_speechtoken(speech)
        return speech_token, speech_token_len

    def _speech_2_speechtoken(self, speech):
        assert (
            speech.shape[1] / 16000 <= 30
        ), "do not support extract speech token for audio longer than 30s"
        feat = whisper.log_mel_spectrogram(speech, n_mels=128)
        speech_token = (
            self.speech_tokenizer_session.run(
                None,
                {
                    self.speech_tokenizer_session.get_inputs()[0]
                    .name: feat.detach()
                    .cpu()
                    .numpy(),
                    self.speech_tokenizer_session.get_inputs()[1].name: np.array(
                        [feat.shape[2]], dtype=np.int32
                    ),
                },
            )[0]
            .flatten()
            .tolist()
        )
        speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device)
        speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(
            self.device
        )
        return speech_token, speech_token_len

    def _extract_spk_embedding(self, prompt_wav):
        speech = load_wav(prompt_wav, 16000)
        feat = kaldi.fbank(speech, num_mel_bins=80, dither=0, sample_frequency=16000)
        feat = feat - feat.mean(dim=0, keepdim=True)
        embedding = (
            self.campplus_session.run(
                None,
                {
                    self.campplus_session.get_inputs()[0]
                    .name: feat.unsqueeze(dim=0)
                    .cpu()
                    .numpy()
                },
            )[0]
            .flatten()
            .tolist()
        )
        embedding = torch.tensor([embedding]).to(self.device)
        return embedding

    def _extract_speech_feat(self, prompt_wav):
        speech = load_wav(prompt_wav, 24000)
        speech_feat = (
            self.feat_extractor(speech, **self.mel_config)
            .squeeze(dim=0)
            .transpose(0, 1)
            .to(self.device)
        )
        speech_feat = speech_feat.unsqueeze(dim=0)
        speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(
            self.device
        )
        return speech_feat, speech_feat_len

    def text_normalize(self, text, split=True, text_frontend=True):
        if isinstance(text, Generator):
            logging.info("get tts_text generator, will skip text_normalize!")
            return [text]
        # NOTE skip text_frontend when ssml symbol in text
        if "<|" in text and "|>" in text:
            text_frontend = False
        if text_frontend is False or text == "":
            return [text] if split is True else text
        text = text.strip()
        if self.text_frontend == "ttsfrd":
            texts = [
                i["text"]
                for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]
            ]
            text = "".join(texts)
        else:
            if contains_chinese(text):
                if self.text_frontend == "wetext":
                    text = self.zh_tn_model.normalize(text)
                text = text.replace("\n", "")
                text = replace_blank(text)
                text = replace_corner_mark(text)
                text = text.replace(".", "。")
                text = text.replace(" - ", "，")
                text = remove_bracket(text)
                text = re.sub(r"[，,、]+$", "。", text)
                texts = list(
                    split_paragraph(
                        text,
                        partial(
                            self.tokenizer.encode, allowed_special=self.allowed_special
                        ),
                        "zh",
                        token_max_n=80,
                        token_min_n=60,
                        merge_len=20,
                        comma_split=False,
                    )
                )
            else:
                if self.text_frontend == "wetext":
                    text = self.en_tn_model.normalize(text)
                text = spell_out_number(text, self.inflect_parser)
                texts = list(
                    split_paragraph(
                        text,
                        partial(
                            self.tokenizer.encode, allowed_special=self.allowed_special
                        ),
                        "en",
                        token_max_n=80,
                        token_min_n=60,
                        merge_len=20,
                        comma_split=False,
                    )
                )
        texts = [i for i in texts if not is_only_punctuation(i)]
        return texts if split is True else text

    def frontend_sft(self, tts_text, spk_id):
        tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
        embedding = self.spk2info[spk_id]["embedding"]
        model_input = {
            "text": tts_text_token,
            "text_len": tts_text_token_len,
            "llm_embedding": embedding,
            "flow_embedding": embedding,
        }
        return model_input

    def frontend_zero_shot(
        self, tts_text, prompt_text, prompt_wav, resample_rate, zero_shot_spk_id
    ):
        tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
        if zero_shot_spk_id == "":
            prompt_text_token, prompt_text_token_len = self._extract_text_token(
                prompt_text
            )
            speech_feat, speech_feat_len = self._extract_speech_feat(prompt_wav)
            speech_token, speech_token_len = self._extract_speech_token(prompt_wav)
            if resample_rate == 24000:
                # cosyvoice2, force speech_feat % speech_token = 2
                token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1])
                speech_feat, speech_feat_len[:] = (
                    speech_feat[:, : 2 * token_len],
                    2 * token_len,
                )
                speech_token, speech_token_len[:] = (
                    speech_token[:, :token_len],
                    token_len,
                )
            embedding = self._extract_spk_embedding(prompt_wav)
            model_input = {
                "prompt_text": prompt_text_token,
                "prompt_text_len": prompt_text_token_len,
                "llm_prompt_speech_token": speech_token,
                "llm_prompt_speech_token_len": speech_token_len,
                "flow_prompt_speech_token": speech_token,
                "flow_prompt_speech_token_len": speech_token_len,
                "prompt_speech_feat": speech_feat,
                "prompt_speech_feat_len": speech_feat_len,
                "llm_embedding": embedding,
                "flow_embedding": embedding,
            }
        else:
            model_input = {**self.spk2info[zero_shot_spk_id]}
        model_input["text"] = tts_text_token
        model_input["text_len"] = tts_text_token_len
        return model_input

    def frontend_cross_lingual(
        self, tts_text, prompt_wav, resample_rate, zero_shot_spk_id
    ):
        model_input = self.frontend_zero_shot(
            tts_text, "", prompt_wav, resample_rate, zero_shot_spk_id
        )
        # in cross lingual mode, we remove prompt in llm
        del model_input["prompt_text"]
        del model_input["prompt_text_len"]
        del model_input["llm_prompt_speech_token"]
        del model_input["llm_prompt_speech_token_len"]
        return model_input

    def frontend_instruct(self, tts_text, spk_id, instruct_text):
        model_input = self.frontend_sft(tts_text, spk_id)
        # in instruct mode, we remove spk_embedding in llm due to information leakage
        del model_input["llm_embedding"]
        instruct_text_token, instruct_text_token_len = self._extract_text_token(
            instruct_text
        )
        model_input["prompt_text"] = instruct_text_token
        model_input["prompt_text_len"] = instruct_text_token_len
        return model_input

    def frontend_instruct2(
        self, tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id
    ):
        model_input = self.frontend_zero_shot(
            tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id
        )
        del model_input["llm_prompt_speech_token"]
        del model_input["llm_prompt_speech_token_len"]
        return model_input

    def frontend_vc(self, source_speech_16k, prompt_wav, resample_rate):
        prompt_speech_token, prompt_speech_token_len = self._extract_speech_token(
            prompt_wav
        )
        prompt_speech_feat, prompt_speech_feat_len = self._extract_speech_feat(
            prompt_wav
        )
        embedding = self._extract_spk_embedding(prompt_wav)
        source_speech_token, source_speech_token_len = self._extract_speech_token(
            source_speech_16k
        )
        model_input = {
            "source_speech_token": source_speech_token,
            "source_speech_token_len": source_speech_token_len,
            "flow_prompt_speech_token": prompt_speech_token,
            "flow_prompt_speech_token_len": prompt_speech_token_len,
            "prompt_speech_feat": prompt_speech_feat,
            "prompt_speech_feat_len": prompt_speech_feat_len,
            "flow_embedding": embedding,
        }
        return model_input