feat: Integrate IndexTTS2 model and update related schemas and frontend components
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141
qwen3-tts-backend/indextts/s2mel/modules/length_regulator.py
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141
qwen3-tts-backend/indextts/s2mel/modules/length_regulator.py
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from typing import Tuple
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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from indextts.s2mel.modules.commons import sequence_mask
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import numpy as np
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from indextts.s2mel.dac.nn.quantize import VectorQuantize
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# f0_bin = 256
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f0_max = 1100.0
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f0_min = 50.0
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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def f0_to_coarse(f0, f0_bin):
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f0_mel = 1127 * (1 + f0 / 700).log()
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a = (f0_bin - 2) / (f0_mel_max - f0_mel_min)
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b = f0_mel_min * a - 1.
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f0_mel = torch.where(f0_mel > 0, f0_mel * a - b, f0_mel)
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# torch.clip_(f0_mel, min=1., max=float(f0_bin - 1))
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f0_coarse = torch.round(f0_mel).long()
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f0_coarse = f0_coarse * (f0_coarse > 0)
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f0_coarse = f0_coarse + ((f0_coarse < 1) * 1)
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f0_coarse = f0_coarse * (f0_coarse < f0_bin)
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f0_coarse = f0_coarse + ((f0_coarse >= f0_bin) * (f0_bin - 1))
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return f0_coarse
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class InterpolateRegulator(nn.Module):
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def __init__(
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self,
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channels: int,
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sampling_ratios: Tuple,
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is_discrete: bool = False,
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in_channels: int = None, # only applies to continuous input
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vector_quantize: bool = False, # whether to use vector quantization, only applies to continuous input
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codebook_size: int = 1024, # for discrete only
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out_channels: int = None,
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groups: int = 1,
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n_codebooks: int = 1, # number of codebooks
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quantizer_dropout: float = 0.0, # dropout for quantizer
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f0_condition: bool = False,
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n_f0_bins: int = 512,
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):
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super().__init__()
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self.sampling_ratios = sampling_ratios
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out_channels = out_channels or channels
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model = nn.ModuleList([])
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if len(sampling_ratios) > 0:
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self.interpolate = True
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for _ in sampling_ratios:
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module = nn.Conv1d(channels, channels, 3, 1, 1)
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norm = nn.GroupNorm(groups, channels)
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act = nn.Mish()
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model.extend([module, norm, act])
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else:
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self.interpolate = False
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model.append(
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nn.Conv1d(channels, out_channels, 1, 1)
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)
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self.model = nn.Sequential(*model)
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self.embedding = nn.Embedding(codebook_size, channels)
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self.is_discrete = is_discrete
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self.mask_token = nn.Parameter(torch.zeros(1, channels))
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self.n_codebooks = n_codebooks
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if n_codebooks > 1:
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self.extra_codebooks = nn.ModuleList([
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nn.Embedding(codebook_size, channels) for _ in range(n_codebooks - 1)
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])
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self.extra_codebook_mask_tokens = nn.ParameterList([
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nn.Parameter(torch.zeros(1, channels)) for _ in range(n_codebooks - 1)
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])
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self.quantizer_dropout = quantizer_dropout
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if f0_condition:
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self.f0_embedding = nn.Embedding(n_f0_bins, channels)
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self.f0_condition = f0_condition
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self.n_f0_bins = n_f0_bins
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self.f0_bins = torch.arange(2, 1024, 1024 // n_f0_bins)
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self.f0_mask = nn.Parameter(torch.zeros(1, channels))
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else:
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self.f0_condition = False
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if not is_discrete:
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self.content_in_proj = nn.Linear(in_channels, channels)
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if vector_quantize:
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self.vq = VectorQuantize(channels, codebook_size, 8)
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def forward(self, x, ylens=None, n_quantizers=None, f0=None):
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# apply token drop
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if self.training:
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n_quantizers = torch.ones((x.shape[0],)) * self.n_codebooks
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dropout = torch.randint(1, self.n_codebooks + 1, (x.shape[0],))
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n_dropout = int(x.shape[0] * self.quantizer_dropout)
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n_quantizers[:n_dropout] = dropout[:n_dropout]
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n_quantizers = n_quantizers.to(x.device)
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# decide whether to drop for each sample in batch
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else:
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n_quantizers = torch.ones((x.shape[0],), device=x.device) * (self.n_codebooks if n_quantizers is None else n_quantizers)
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if self.is_discrete:
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if self.n_codebooks > 1:
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assert len(x.size()) == 3
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x_emb = self.embedding(x[:, 0])
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for i, emb in enumerate(self.extra_codebooks):
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x_emb = x_emb + (n_quantizers > i+1)[..., None, None] * emb(x[:, i+1])
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# add mask token if not using this codebook
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# x_emb = x_emb + (n_quantizers <= i+1)[..., None, None] * self.extra_codebook_mask_tokens[i]
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x = x_emb
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elif self.n_codebooks == 1:
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if len(x.size()) == 2:
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x = self.embedding(x)
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else:
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x = self.embedding(x[:, 0])
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else:
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x = self.content_in_proj(x)
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# x in (B, T, D)
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mask = sequence_mask(ylens).unsqueeze(-1)
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if self.interpolate:
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x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
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else:
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x = x.transpose(1, 2).contiguous()
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mask = mask[:, :x.size(2), :]
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ylens = ylens.clamp(max=x.size(2)).long()
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if self.f0_condition:
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if f0 is None:
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x = x + self.f0_mask.unsqueeze(-1)
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else:
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#quantized_f0 = torch.bucketize(f0, self.f0_bins.to(f0.device)) # (N, T)
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quantized_f0 = f0_to_coarse(f0, self.n_f0_bins)
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quantized_f0 = quantized_f0.clamp(0, self.n_f0_bins - 1).long()
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f0_emb = self.f0_embedding(quantized_f0)
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f0_emb = F.interpolate(f0_emb.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
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x = x + f0_emb
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out = self.model(x).transpose(1, 2).contiguous()
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if hasattr(self, 'vq'):
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out_q, commitment_loss, codebook_loss, codes, out, = self.vq(out.transpose(1, 2))
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out_q = out_q.transpose(1, 2)
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return out_q * mask, ylens, codes, commitment_loss, codebook_loss
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olens = ylens
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return out * mask, olens, None, None, None
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