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from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from torchaudio import transforms as T
from huggingface_hub import PyTorchModelHubMixin, ModelHubMixin, hf_hub_download
from transformers import AutoFeatureExtractor, HubertModel, Wav2Vec2BertModel
from .codec_encoder import CodecEncoder
from .codec_encoder_distill import DistillCodecEncoder
from .codec_decoder_vocos import CodecDecoderVocos
from .module import SemanticEncoder
class NeuCodec(
nn.Module,
PyTorchModelHubMixin,
repo_url="https://github.com/neuphonic/neucodec",
license="apache-2.0",
):
def __init__(self, sample_rate: int, hop_length: int, decoder_depth: int = 12):
super().__init__()
self.sample_rate = sample_rate
self.hop_length = hop_length
self.semantic_model = Wav2Vec2BertModel.from_pretrained(
"facebook/w2v-bert-2.0", output_hidden_states=True
)
self.feature_extractor = AutoFeatureExtractor.from_pretrained(
"facebook/w2v-bert-2.0"
)
self.SemanticEncoder_module = SemanticEncoder(1024, 1024, 1024)
self.CodecEnc = CodecEncoder()
self.generator = CodecDecoderVocos(hop_length=hop_length, depth=decoder_depth)
self.fc_prior = nn.Linear(2048, 2048)
self.fc_post_a = nn.Linear(2048, 1024)
@property
def device(self):
return next(self.parameters()).device
@classmethod
def _from_pretrained(
cls,
*,
model_id: str = None,
revision: Optional[str] = None,
cache_dir: Optional[str] = None,
force_download: bool = False,
proxies: Optional[Dict] = None,
resume_download: bool = False,
local_files_only: bool = False,
token: Optional[str] = None,
map_location: str = "cpu",
strict: bool = False,
local_ckpt_path: str = None,
**model_kwargs,
):
if model_id == "neuphonic/neucodec":
ignore_keys = ["fc_post_s", "SemanticDecoder"]
elif model_id == "neuphonic/distill-neucodec":
ignore_keys = []
else:
ignore_keys = []
if model_id is not None:
ckpt_path = hf_hub_download(
repo_id=model_id,
filename="pytorch_model.bin",
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
)
else:
# incase we interpolate the weight to become 960 instead train from scratch
ckpt_path = local_ckpt_path
# initialize model
decoder_depth = model_kwargs.pop('decoder_depth', 12)
model = cls(44_100, 882, decoder_depth=decoder_depth)
# load weights
state_dict = torch.load(ckpt_path, map_location)
contains_list = lambda s, l: any(i in s for i in l)
state_dict = {
k:v for k, v in state_dict.items()
if not contains_list(k, ignore_keys)
}
# Filter out keys with shape mismatches (e.g. 48k model vs 24k checkpoint)
model_state = model.state_dict()
state_dict = {
k: v for k, v in state_dict.items()
if k in model_state and v.shape == model_state[k].shape
}
model.load_state_dict(state_dict, strict=False)
return model
def _prepare_audio(self, audio_or_path: torch.Tensor | Path | str):
# load from file
if isinstance(audio_or_path, (Path, str)):
y, sr = torchaudio.load(audio_or_path)
if sr != 16_000:
y, sr = (T.Resample(sr, 16_000)(y), 16_000)
y = y[None, :] # [1, T] -> [B, 1, T]
# ensure input tensor is of correct shape
elif isinstance(audio_or_path, torch.Tensor):
y = audio_or_path
if len(y.shape) == 3:
y = audio_or_path
else:
raise ValueError(
f"NeuCodec expects tensor audio input to be of shape [B, 1, T] -- received shape: {y.shape}"
)
# pad audio
pad_for_wav = 320 - (y.shape[-1] % 320)
y = torch.nn.functional.pad(y, (0, pad_for_wav))
return y
def encode_code(self, audio_or_path: torch.Tensor | Path | str) -> torch.Tensor:
"""
Args:
audio_or_path: torch.Tensor [B, 1, T] | Path | str, input audio
Returns:
fsq_codes: torch.Tensor [B, 1, F], 50hz FSQ codes
"""
# prepare inputs
y = self._prepare_audio(audio_or_path)
semantic_features = self.feature_extractor(
[w for w in y.squeeze(1).cpu()], sampling_rate=16_000, return_tensors="pt"
).input_features.to(self.device)
# acoustic encoding
acoustic_emb = self.CodecEnc(y.to(self.device))
acoustic_emb = acoustic_emb.transpose(1, 2)
# semantic encoding
semantic_output = (
self.semantic_model(semantic_features).hidden_states[16].transpose(1, 2)
)
semantic_encoded = self.SemanticEncoder_module(semantic_output)
# concatenate embeddings
if acoustic_emb.shape[-1] != semantic_encoded.shape[-1]:
min_len = min(acoustic_emb.shape[-1], semantic_encoded.shape[-1])
acoustic_emb = acoustic_emb[:, :, :min_len]
semantic_encoded = semantic_encoded[:, :, :min_len]
concat_emb = torch.cat([semantic_encoded, acoustic_emb], dim=1)
concat_emb = self.fc_prior(concat_emb.transpose(1, 2)).transpose(1, 2)
# quantize
_, fsq_codes, _ = self.generator(concat_emb, vq=True)
return fsq_codes
def encode_code_from_features(self, audio: torch.Tensor, semantic_features: torch.Tensor) -> torch.Tensor:
"""Encode using pre-computed semantic features, avoiding CPU feature extraction.
Args:
audio: torch.Tensor [B, 1, T], 16kHz input audio
semantic_features: torch.Tensor [B, seq_len, feat_dim], pre-computed features
Returns:
fsq_codes: torch.Tensor [B, 1, F], 50hz FSQ codes
"""
y = self._prepare_audio(audio)
semantic_features = semantic_features.to(self.device)
# acoustic encoding
acoustic_emb = self.CodecEnc(y.to(self.device))
acoustic_emb = acoustic_emb.transpose(1, 2)
# semantic encoding
semantic_output = (
self.semantic_model(semantic_features).hidden_states[16].transpose(1, 2)
)
semantic_encoded = self.SemanticEncoder_module(semantic_output)
# concatenate embeddings
if acoustic_emb.shape[-1] != semantic_encoded.shape[-1]:
min_len = min(acoustic_emb.shape[-1], semantic_encoded.shape[-1])
acoustic_emb = acoustic_emb[:, :, :min_len]
semantic_encoded = semantic_encoded[:, :, :min_len]
concat_emb = torch.cat([semantic_encoded, acoustic_emb], dim=1)
concat_emb = self.fc_prior(concat_emb.transpose(1, 2)).transpose(1, 2)
# quantize
_, fsq_codes, _ = self.generator(concat_emb, vq=True)
return fsq_codes
def decode_code(self, fsq_codes: torch.Tensor) -> torch.Tensor:
"""
Args:
fsq_codes: torch.Tensor [B, 1, F], 50hz FSQ codes
Returns:
recon: torch.Tensor [B, 1, T], reconstructed 48kHz audio
"""
fsq_post_emb = self.generator.quantizer.get_output_from_indices(fsq_codes.transpose(1, 2))
fsq_post_emb = fsq_post_emb.transpose(1, 2)
fsq_post_emb = self.fc_post_a(fsq_post_emb.transpose(1, 2)).transpose(1, 2)
recon = self.generator(fsq_post_emb.transpose(1, 2), vq=False)[0]
return recon
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