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# Copyright (c) Kyutai, all rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Retrieves the pretrained models for Moshi and Mimi."""
from pathlib import Path
from safetensors.torch import load_model
import torch
from moshi.models.compression import MimiModel
from moshi.models.lm import LMModel
from moshi.modules import SEANetEncoder, SEANetDecoder, transformer
from moshi.quantization import SplitResidualVectorQuantizer
SAMPLE_RATE = 24000
FRAME_RATE = 12.5
TEXT_TOKENIZER_NAME = 'tokenizer_spm_32k_3.model'
MOSHI_NAME = 'model.safetensors'
MIMI_NAME = 'tokenizer-e351c8d8-checkpoint125.safetensors'
DEFAULT_REPO = 'kyutai/moshiko-pytorch-bf16'
_seanet_kwargs = {
"channels": 1,
"dimension": 512,
"causal": True,
"n_filters": 64,
"n_residual_layers": 1,
"activation": "ELU",
"compress": 2,
"dilation_base": 2,
"disable_norm_outer_blocks": 0,
"kernel_size": 7,
"residual_kernel_size": 3,
"last_kernel_size": 3,
# We train using weight_norm but then the weights are pre-processed for inference so
# that we can use a normal convolution.
"norm": "none",
"pad_mode": "constant",
"ratios": [8, 6, 5, 4],
"true_skip": True,
}
_quantizer_kwargs = {
"dimension": 256,
"n_q": 32,
"bins": 2048,
"input_dimension": _seanet_kwargs["dimension"],
"output_dimension": _seanet_kwargs["dimension"],
}
_transformer_kwargs = {
"d_model": _seanet_kwargs["dimension"],
"num_heads": 8,
"num_layers": 8,
"causal": True,
"layer_scale": 0.01,
"context": 250,
"conv_layout": True,
"max_period": 10000,
"gating": "none",
"norm": "layer_norm",
"positional_embedding": "rope",
"dim_feedforward": 2048,
"input_dimension": _seanet_kwargs["dimension"],
"output_dimensions": [_seanet_kwargs["dimension"]],
}
_lm_kwargs = {
"dim": 4096,
"text_card": 32000,
"existing_text_padding_id": 3,
"n_q": 16,
"dep_q": 8,
"card": _quantizer_kwargs["bins"],
"num_heads": 32,
"num_layers": 32,
"hidden_scale": 4.125,
"causal": True,
"layer_scale": None,
"context": 3000,
"max_period": 10000,
"gating": "silu",
"norm": "rms_norm_f32",
"positional_embedding": "rope",
"depformer_dim": 1024,
"depformer_dim_feedforward": int(4.125 * 1024),
"depformer_num_heads": 16,
"depformer_num_layers": 6,
"depformer_causal": True,
"depformer_layer_scale": None,
"depformer_multi_linear": True,
"depformer_context": 8,
"depformer_max_period": 10000,
"depformer_gating": "silu",
"depformer_pos_emb": "none",
"depformer_weights_per_step": True,
"delays": [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1],
}
def _is_safetensors(path: Path | str) -> bool:
return Path(path).suffix in (".safetensors", ".sft", ".sfts")
def get_mimi(filename: str | Path,
device: torch.device | str = 'cpu') -> MimiModel:
"""Return a pretrained Mimi model."""
encoder = SEANetEncoder(**_seanet_kwargs)
decoder = SEANetDecoder(**_seanet_kwargs)
encoder_transformer = transformer.ProjectedTransformer(
device=device, **_transformer_kwargs
)
decoder_transformer = transformer.ProjectedTransformer(
device=device, **_transformer_kwargs
)
quantizer = SplitResidualVectorQuantizer(
**_quantizer_kwargs,
)
model = MimiModel(
encoder,
decoder,
quantizer,
channels=1,
sample_rate=SAMPLE_RATE,
frame_rate=FRAME_RATE,
encoder_frame_rate=SAMPLE_RATE / encoder.hop_length,
causal=True,
resample_method="conv",
encoder_transformer=encoder_transformer,
decoder_transformer=decoder_transformer,
).to(device=device)
model.eval()
if _is_safetensors(filename):
load_model(model, filename)
else:
pkg = torch.load(filename, "cpu")
model.load_state_dict(pkg["model"])
model.set_num_codebooks(8)
return model
def get_moshi_lm(filename: str | Path,
device: torch.device | str = 'cpu') -> LMModel:
dtype = torch.bfloat16
model = LMModel(
device=device,
dtype=dtype,
**_lm_kwargs,
).to(device=device, dtype=dtype)
model.eval()
if _is_safetensors(filename):
load_model(model, filename)
else:
pkg = torch.load(
filename,
"cpu",
)
model.load_state_dict(pkg["fsdp_best_state"]["model"])
return model
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