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https://github.com/k2-fsa/icefall.git
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create model from parameters
This commit is contained in:
Fangjun Kuang
parent
f95ac12d70
commit
6fac3a3143
@ -4,9 +4,9 @@ import torch
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import torch.nn.functional as F
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from matcha.models.components.decoder import Decoder
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from matcha.utils.pylogger import get_pylogger
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# from matcha.utils.pylogger import get_pylogger
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log = get_pylogger(__name__)
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# log = get_pylogger(__name__)
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class BASECFM(torch.nn.Module, ABC):
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@ -6,10 +6,10 @@ import torch
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import torch.nn as nn
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from einops import rearrange
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import matcha.utils as utils
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# import matcha.utils as utils
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from matcha.utils.model import sequence_mask
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log = utils.get_pylogger(__name__)
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# log = utils.get_pylogger(__name__)
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class LayerNorm(nn.Module):
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@ -5,8 +5,8 @@ import random
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import torch
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import matcha.utils.monotonic_align as monotonic_align
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from matcha import utils
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from matcha.models.baselightningmodule import BaseLightningClass
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# from matcha import utils
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# from matcha.models.baselightningmodule import BaseLightningClass
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from matcha.models.components.flow_matching import CFM
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from matcha.models.components.text_encoder import TextEncoder
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from matcha.utils.model import (
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@ -17,10 +17,10 @@ from matcha.utils.model import (
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sequence_mask,
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)
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log = utils.get_pylogger(__name__)
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# log = utils.get_pylogger(__name__)
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class MatchaTTS(BaseLightningClass): # 🍵
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class MatchaTTS(torch.nn.Module): # 🍵
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def __init__(
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self,
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n_vocab,
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@ -30,7 +30,7 @@ class MatchaTTS(BaseLightningClass): # 🍵
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encoder,
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decoder,
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cfm,
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data_statistics,
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# data_statistics,
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out_size,
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optimizer=None,
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scheduler=None,
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@ -39,7 +39,7 @@ class MatchaTTS(BaseLightningClass): # 🍵
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):
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super().__init__()
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self.save_hyperparameters(logger=False)
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# self.save_hyperparameters(logger=False)
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self.n_vocab = n_vocab
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self.n_spks = n_spks
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@ -70,7 +70,7 @@ class MatchaTTS(BaseLightningClass): # 🍵
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spk_emb_dim=spk_emb_dim,
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)
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self.update_data_statistics(data_statistics)
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# self.update_data_statistics(data_statistics)
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@torch.inference_mode()
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def synthesise(self, x, x_lengths, n_timesteps, temperature=1.0, spks=None, length_scale=1.0):
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98
egs/ljspeech/TTS/matcha/train.py
Executable file
98
egs/ljspeech/TTS/matcha/train.py
Executable file
@ -0,0 +1,98 @@
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#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
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import torch
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from icefall.utils import AttributeDict
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from matcha.models.matcha_tts import MatchaTTS
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def get_model(params):
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m = MatchaTTS(**params.model)
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return m
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def main():
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n_feats = 80
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filter_channels_dp = 256
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encoder_params_p_dropout = 0.1
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params = AttributeDict(
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{
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"model": AttributeDict(
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{
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"n_vocab": 178,
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"n_spks": 1, # for ljspeech.
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"spk_emb_dim": 64,
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"n_feats": n_feats,
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"out_size": None, # or use 172
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"prior_loss": True,
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"use_precomputed_durations": False,
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"encoder": AttributeDict(
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{
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"encoder_type": "RoPE Encoder", # not used
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"encoder_params": AttributeDict(
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{
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"n_feats": n_feats,
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"n_channels": 192,
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"filter_channels": 768,
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"filter_channels_dp": filter_channels_dp,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": encoder_params_p_dropout,
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"spk_emb_dim": 64,
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"n_spks": 1,
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"prenet": True,
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}
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),
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"duration_predictor_params": AttributeDict(
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{
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"filter_channels_dp": filter_channels_dp,
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"kernel_size": 3,
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"p_dropout": encoder_params_p_dropout,
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}
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),
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}
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),
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"decoder": AttributeDict(
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{
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"channels": [256, 256],
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"dropout": 0.05,
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"attention_head_dim": 64,
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"n_blocks": 1,
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"num_mid_blocks": 2,
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"num_heads": 2,
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"act_fn": "snakebeta",
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}
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),
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"cfm": AttributeDict(
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{
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"name": "CFM",
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"solver": "euler",
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"sigma_min": 1e-4,
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}
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),
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"optimizer": AttributeDict(
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{
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"lr": 1e-4,
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"weight_decay": 0.0,
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}
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),
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}
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)
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}
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)
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m = get_model(params)
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print(m)
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num_param = sum([p.numel() for p in m.parameters()])
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print(f"Number of parameters: {num_param}")
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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if __name__ == "__main__":
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main()
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@ -1,5 +1,5 @@
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from matcha.utils.instantiators import instantiate_callbacks, instantiate_loggers
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from matcha.utils.logging_utils import log_hyperparameters
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from matcha.utils.pylogger import get_pylogger
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from matcha.utils.rich_utils import enforce_tags, print_config_tree
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from matcha.utils.utils import extras, get_metric_value, task_wrapper
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# from matcha.utils.instantiators import instantiate_callbacks, instantiate_loggers
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# from matcha.utils.logging_utils import log_hyperparameters
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# from matcha.utils.pylogger import get_pylogger
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# from matcha.utils.rich_utils import enforce_tags, print_config_tree
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# from matcha.utils.utils import extras, get_metric_value, task_wrapper
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3
egs/ljspeech/TTS/matcha/utils/monotonic_align/.gitignore
vendored
Normal file
3
egs/ljspeech/TTS/matcha/utils/monotonic_align/.gitignore
vendored
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@ -0,0 +1,3 @@
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build
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core.c
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*.so
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