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# SPDX-License-Identifier: Apache-2.0
"""Qwen3-TTS 12.5 Hz waveform decoder for the EdgeInstant model."""
from __future__ import annotations
import json
import math
from pathlib import Path
import torch
from torch import nn
from torch.nn import functional as F
from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import (
Qwen3OmniMoeCode2WavConfig,
)
from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import (
Qwen3OmniMoeCausalConvNet,
Qwen3OmniMoeCode2WavDecoderResidualUnit,
Qwen3OmniMoeCode2WavTransformerModel,
Qwen3OmniMoeConvNeXtBlock,
SnakeBeta,
)
class _CausalTransConvNet(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1):
super().__init__()
self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride=stride)
self.right_pad = kernel_size - stride
def forward(self, hidden):
hidden = self.conv(hidden)
if self.right_pad:
hidden = hidden[..., : -self.right_pad]
return hidden.contiguous()
class _Codebook(nn.Module):
def __init__(self, dim, size):
super().__init__()
self.cluster_usage = nn.Parameter(torch.ones(size))
self.embedding_sum = nn.Parameter(torch.zeros(size, dim))
def forward(self, codes):
embedding = self.embedding_sum / self.cluster_usage.clamp(min=1e-5)[:, None]
return F.embedding(codes, embedding)
class _VectorQuantization(nn.Module):
def __init__(self, dim, size):
super().__init__()
self._codebook = _Codebook(dim, size)
def forward(self, codes):
return self._codebook(codes).transpose(1, 2)
class _ResidualVectorQuantization(nn.Module):
def __init__(self, count, dim, size):
super().__init__()
self.layers = nn.ModuleList([_VectorQuantization(dim, size) for _ in range(count)])
def forward(self, codes):
quantized = torch.zeros([1], device=codes.device)[0]
for index, layer in enumerate(self.layers):
quantized = quantized + layer(codes[:, index])
return quantized
class _ResidualVectorQuantizer(nn.Module):
def __init__(self, count, config, *, inference_only=False):
super().__init__()
dim = config.codebook_dim // 2
self.input_proj = (
None if inference_only else nn.Conv1d(config.codebook_dim, dim, 1, bias=False)
)
self.output_proj = nn.Conv1d(dim, config.codebook_dim, 1, bias=False)
self.vq = _ResidualVectorQuantization(count, dim, config.codebook_size)
def forward(self, codes):
return self.output_proj(self.vq(codes))
class _SplitResidualVectorQuantizer(nn.Module):
def __init__(self, config, *, inference_only=False):
super().__init__()
self.rvq_first = _ResidualVectorQuantizer(1, config, inference_only=inference_only)
self.rvq_rest = _ResidualVectorQuantizer(
config.num_quantizers - 1, config, inference_only=inference_only,
)
def forward(self, codes):
quantized = self.rvq_first(codes[:, :1])
quantized += self.rvq_rest(codes[:, 1:])
return quantized
class _ProjectedTransformer(Qwen3OmniMoeCode2WavTransformerModel):
def __init__(self, config):
super().__init__(config)
self.input_proj = nn.Linear(config.latent_dim, config.hidden_size)
self.output_proj = nn.Linear(config.hidden_size, config.latent_dim)
def forward(self, hidden):
positions = torch.arange(hidden.shape[1], device=hidden.device)
distances = positions[:, None] - positions[None, :]
mask = ((distances >= 0) & (distances < self.config.sliding_window))[None, None]
output = super().forward(
inputs_embeds=self.input_proj(hidden),
attention_mask={"sliding_attention": mask.expand(hidden.shape[0], -1, -1, -1)},
use_cache=False,
)
return self.output_proj(output.last_hidden_state)
class _DecoderBlock(nn.Module):
def __init__(self, config, index):
super().__init__()
input_dim = config.decoder_dim // 2**index
output_dim = input_dim // 2
rate = config.upsample_rates[index]
self.block = nn.ModuleList(
[SnakeBeta(input_dim), _CausalTransConvNet(input_dim, output_dim, 2 * rate, rate)]
+ [Qwen3OmniMoeCode2WavDecoderResidualUnit(output_dim, dilation) for dilation in (1, 3, 9)]
)
def forward(self, hidden):
for block in self.block:
hidden = block(hidden)
return hidden
class EdgeInstantCodecDecoder(nn.Module):
"""Decode integer codes ``[batch, frames, 16]`` to float audio ``[batch, samples]``.
``config`` is the complete Qwen3-TTS speech tokenizer configuration. Weights
match its ``decoder.*`` tensors after removing that prefix. Setting its
top-level ``inference_only`` field omits the unused quantizer input projections.
"""
def __init__(self, config: dict):
super().__init__()
self.sample_rate = int(config["output_sample_rate"])
self.samples_per_frame = int(config["decode_upsample_rate"])
decoder_config = dict(config["decoder_config"])
decoder_config["rope_parameters"] = {
"rope_type": "default",
"rope_theta": decoder_config.pop("rope_theta", 10000),
}
self.config = Qwen3OmniMoeCode2WavConfig(**decoder_config)
self.config._attn_implementation = "sdpa"
cfg = self.config
if math.prod([*cfg.upsample_rates, *cfg.upsampling_ratios]) != self.samples_per_frame:
raise ValueError("decode_upsample_rate must match the waveform decoder upsampling factors")
self.pre_transformer = _ProjectedTransformer(cfg)
self.quantizer = _SplitResidualVectorQuantizer(
cfg, inference_only=bool(config.get("inference_only", False)),
)
self.pre_conv = Qwen3OmniMoeCausalConvNet(cfg.codebook_dim, cfg.latent_dim, 3)
self.upsample = nn.ModuleList(
[
nn.ModuleList(
[
_CausalTransConvNet(cfg.latent_dim, cfg.latent_dim, factor, factor),
Qwen3OmniMoeConvNeXtBlock(cfg.latent_dim),
]
)
for factor in cfg.upsampling_ratios
]
)
output_dim = cfg.decoder_dim // 2 ** len(cfg.upsample_rates)
self.decoder = nn.ModuleList(
[Qwen3OmniMoeCausalConvNet(cfg.latent_dim, cfg.decoder_dim, 7)]
+ [_DecoderBlock(cfg, index) for index in range(len(cfg.upsample_rates))]
+ [SnakeBeta(output_dim), Qwen3OmniMoeCausalConvNet(output_dim, 1, 7)]
)
def forward(self, codes: torch.Tensor) -> torch.Tensor:
if codes.ndim != 3 or codes.shape[-1] != self.config.num_quantizers:
raise ValueError(f"codes must be [batch, frames, {self.config.num_quantizers}]")
if codes.shape[1] == 0:
return self.pre_conv.conv.weight.new_empty((codes.shape[0], 0), dtype=torch.float32)
codes = codes.to(device=self.pre_conv.conv.weight.device, dtype=torch.long)
hidden = self.quantizer(codes.transpose(1, 2))
hidden = self.pre_conv(hidden).transpose(1, 2)
hidden = self.pre_transformer(hidden).transpose(1, 2)
for blocks in self.upsample:
for block in blocks:
hidden = block(hidden)
for block in self.decoder:
hidden = block(hidden)
return hidden.squeeze(1).clamp(min=-1, max=1).float()
def decode(
self, codes: torch.Tensor, *, chunk_size: int = 300, left_context_frames: int = 25
) -> torch.Tensor:
"""Decode with the speech tokenizer's chunking and left context convention."""
if chunk_size <= 0 or left_context_frames < 0:
raise ValueError("chunk_size must be positive and left_context_frames nonnegative")
if codes.shape[1] == 0:
return self(codes)
waves = []
for start in range(0, codes.shape[1], chunk_size):
context = min(start, left_context_frames)
wave = self(codes[:, start - context : start + chunk_size])
waves.append(wave[:, context * self.samples_per_frame :])
return torch.cat(waves, dim=-1)
def load_codec_decoder(
directory: str | Path, *, dtype: torch.dtype = torch.float32, device: str = "cpu"
) -> EdgeInstantCodecDecoder:
"""Load the decoder tensors from a local Qwen3-TTS speech tokenizer directory."""
from safetensors import safe_open
directory = Path(directory)
with (directory / "config.json").open() as handle:
config = json.load(handle)
decoder = EdgeInstantCodecDecoder(config).to(dtype=dtype, device=device)
rotary = decoder.pre_transformer.rotary_emb
rotary.inv_freq, rotary.attention_scaling = rotary.compute_default_rope_parameters(
rotary.config, torch.device(device)
)
rotary.original_inv_freq = rotary.inv_freq.clone()
with safe_open(directory / "model.safetensors", framework="pt", device="cpu") as checkpoint:
state = {
key.removeprefix("decoder."): checkpoint.get_tensor(key)
for key in checkpoint.keys()
if key.startswith("decoder.")
}
decoder.load_state_dict(state, strict=True)
return decoder.eval()
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