rendering/universal_tts/models/cosyvoice/utils/common.py

# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
#               2024 Alibaba Inc (authors: Xiang Lyu)
#               2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
#
# 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.
# Modified from ESPnet(https://github.com/espnet/espnet)
"""Unility functions for Transformer."""

import queue
import random
from typing import List

import numpy as np
import torch

IGNORE_ID = -1

instruct_list = [
    "You are a helpful assistant. 请用广东话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用东北话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用甘肃话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用贵州话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用河南话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用湖北话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用湖南话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用江西话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用闽南话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用宁夏话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用山西话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用陕西话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用山东话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用上海话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用四川话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用天津话表达。<|endofprompt|>",
    "You are a helpful assistant. 请用云南话表达。<|endofprompt|>",
    "You are a helpful assistant. Please say a sentence as loudly as possible.<|endofprompt|>",
    "You are a helpful assistant. Please say a sentence in a very soft voice.<|endofprompt|>",
    "You are a helpful assistant. 请用尽可能慢地语速说一句话。<|endofprompt|>",
    "You are a helpful assistant. 请用尽可能快地语速说一句话。<|endofprompt|>",
    "You are a helpful assistant. 请非常开心地说一句话。<|endofprompt|>",
    "You are a helpful assistant. 请非常伤心地说一句话。<|endofprompt|>",
    "You are a helpful assistant. 请非常生气地说一句话。<|endofprompt|>",
    "You are a helpful assistant. 我想体验一下小猪佩奇风格，可以吗？<|endofprompt|>",
    "You are a helpful assistant. 你可以尝试用机器人的方式解答吗？<|endofprompt|>",
]


def pad_list(xs: List[torch.Tensor], pad_value: int):
    """Perform padding for the list of tensors.

    Args:
        xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
        pad_value (float): Value for padding.

    Returns:
        Tensor: Padded tensor (B, Tmax, `*`).

    Examples:
        >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
        >>> x
        [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
        >>> pad_list(x, 0)
        tensor([[1., 1., 1., 1.],
                [1., 1., 0., 0.],
                [1., 0., 0., 0.]])

    """
    max_len = max([len(item) for item in xs])
    batchs = len(xs)
    ndim = xs[0].ndim
    if ndim == 1:
        pad_res = torch.zeros(batchs, max_len, dtype=xs[0].dtype, device=xs[0].device)
    elif ndim == 2:
        pad_res = torch.zeros(
            batchs, max_len, xs[0].shape[1], dtype=xs[0].dtype, device=xs[0].device
        )
    elif ndim == 3:
        pad_res = torch.zeros(
            batchs,
            max_len,
            xs[0].shape[1],
            xs[0].shape[2],
            dtype=xs[0].dtype,
            device=xs[0].device,
        )
    else:
        raise ValueError(f"Unsupported ndim: {ndim}")
    pad_res.fill_(pad_value)
    for i in range(batchs):
        pad_res[i, : len(xs[i])] = xs[i]
    return pad_res


def th_accuracy(
    pad_outputs: torch.Tensor, pad_targets: torch.Tensor, ignore_label: int
) -> torch.Tensor:
    """Calculate accuracy.

    Args:
        pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
        pad_targets (LongTensor): Target label tensors (B, Lmax).
        ignore_label (int): Ignore label id.

    Returns:
        torch.Tensor: Accuracy value (0.0 - 1.0).

    """
    pad_pred = pad_outputs.view(
        pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1)
    ).argmax(2)
    mask = pad_targets != ignore_label
    numerator = torch.sum(
        pad_pred.masked_select(mask) == pad_targets.masked_select(mask)
    )
    denominator = torch.sum(mask)
    return (numerator / denominator).detach()


import numpy as np


def np_accuracy(
    pad_outputs: np.ndarray, pad_targets: np.ndarray, ignore_label: int
) -> float:
    """
    Args:
        pad_outputs: shape [B*L, D]
        pad_targets: shape [B, L]
        ignore_label: int

    Returns:
        accuracy: float in [0, 1]
    """

    B, L = pad_targets.shape
    D = pad_outputs.shape[1]

    # [B*L, D] -> [B, L, D]
    pad_outputs = pad_outputs.reshape(B, L, D)

    # argmax over vocab dim
    pad_pred = np.argmax(pad_outputs, axis=2)

    mask = pad_targets != ignore_label

    if mask.sum() == 0:
        return 0.0

    numerator = np.sum(pad_pred[mask] == pad_targets[mask])
    denominator = np.sum(mask)

    return float(numerator / denominator)


def get_padding(kernel_size, dilation=1):
    return int((kernel_size * dilation - dilation) / 2)


def init_weights(m, mean=0.0, std=0.01):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        m.weight.data.normal_(mean, std)


# Repetition Aware Sampling in VALL-E 2
def ras_sampling(
    weighted_scores,
    decoded_tokens,
    sampling,
    top_p=0.8,
    top_k=25,
    win_size=10,
    tau_r=0.1,
):
    top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
    rep_num = (
        (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids)
        .sum()
        .item()
    )
    if rep_num >= win_size * tau_r:
        top_ids = random_sampling(weighted_scores, decoded_tokens, sampling)
    return top_ids


def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25):
    prob, indices = [], []
    cum_prob = 0.0
    sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(
        descending=True, stable=True
    )
    for i in range(len(sorted_idx)):
        # sampling both top-p and numbers.
        if cum_prob < top_p and len(prob) < top_k:
            cum_prob += sorted_value[i]
            prob.append(sorted_value[i])
            indices.append(sorted_idx[i])
        else:
            break
    prob = torch.tensor(prob).to(weighted_scores)
    indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device)
    top_ids = indices[prob.multinomial(1, replacement=True)].item()
    return top_ids


def random_sampling(weighted_scores, decoded_tokens, sampling):
    top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True).item()
    return top_ids


def fade_in_out(fade_in_mel, fade_out_mel, window):
    device = fade_in_mel.device
    fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu()
    mel_overlap_len = int(window.shape[0] / 2)
    if fade_in_mel.device == torch.device("cpu"):
        fade_in_mel = fade_in_mel.clone()
    fade_in_mel[..., :mel_overlap_len] = (
        fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len]
        + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:]
    )
    return fade_in_mel.to(device)


def set_all_random_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
    assert mask.dtype == torch.bool
    assert dtype in [torch.float32, torch.bfloat16, torch.float16]
    mask = mask.to(dtype)
    # attention mask bias
    # NOTE(Mddct): torch.finfo jit issues
    #     chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min
    mask = (1.0 - mask) * -1.0e10
    return mask


class TrtContextWrapper:
    def __init__(self, trt_engine, trt_concurrent=1, device="cuda:0"):
        self.trt_context_pool = queue.Queue(maxsize=trt_concurrent)
        self.trt_engine = trt_engine
        for _ in range(trt_concurrent):
            trt_context = trt_engine.create_execution_context()
            trt_stream = torch.cuda.stream(torch.cuda.Stream(device))
            assert (
                trt_context is not None
            ), "failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}".format(
                trt_concurrent
            )
            self.trt_context_pool.put([trt_context, trt_stream])
        assert self.trt_context_pool.empty() is False, "no avaialbe estimator context"

    def acquire_estimator(self):
        return self.trt_context_pool.get(), self.trt_engine

    def release_estimator(self, context, stream):
        self.trt_context_pool.put([context, stream])