v1.0

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/eval.yaml

+# @package _global_
+
+defaults:
+  - _self_
+  - data: mnist # choose datamodule with `test_dataloader()` for evaluation
+  - model: mnist
+  - logger: null
+  - trainer: default
+  - paths: default
+  - extras: default
+  - hydra: default
+
+task_name: "eval"
+
+tags: ["dev"]
+
+# passing checkpoint path is necessary for evaluation
+ckpt_path: ???

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/experiment/hifi_dataset_piper_phonemizer.yaml

+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: hi-fi_en-US_female.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["hi-fi", "single_speaker", "piper_phonemizer", "en_US", "female"]
+
+run_name: hi-fi_en-US_female_piper_phonemizer

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/experiment/ljspeech.yaml

+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: ljspeech.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["ljspeech"]
+
+run_name: ljspeech

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/experiment/ljspeech_from_durations.yaml

+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: ljspeech.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["ljspeech"]
+
+run_name: ljspeech
+
+
+data:
+  load_durations: True
+  batch_size: 64

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/experiment/ljspeech_min_memory.yaml

+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: ljspeech.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["ljspeech"]
+
+run_name: ljspeech_min
+
+
+model:
+  out_size: 172

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/experiment/multispeaker.yaml

+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: vctk.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["multispeaker"]
+
+run_name: multispeaker

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/extras/default.yaml

+# disable python warnings if they annoy you
+ignore_warnings: False
+
+# ask user for tags if none are provided in the config
+enforce_tags: True
+
+# pretty print config tree at the start of the run using Rich library
+print_config: True

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/hparams_search/mnist_optuna.yaml

+# @package _global_
+
+# example hyperparameter optimization of some experiment with Optuna:
+# python train.py -m hparams_search=mnist_optuna experiment=example
+
+defaults:
+  - override /hydra/sweeper: optuna
+
+# choose metric which will be optimized by Optuna
+# make sure this is the correct name of some metric logged in lightning module!
+optimized_metric: "val/acc_best"
+
+# here we define Optuna hyperparameter search
+# it optimizes for value returned from function with @hydra.main decorator
+# docs: https://hydra.cc/docs/next/plugins/optuna_sweeper
+hydra:
+  mode: "MULTIRUN" # set hydra to multirun by default if this config is attached
+
+  sweeper:
+    _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
+
+    # storage URL to persist optimization results
+    # for example, you can use SQLite if you set 'sqlite:///example.db'
+    storage: null
+
+    # name of the study to persist optimization results
+    study_name: null
+
+    # number of parallel workers
+    n_jobs: 1
+
+    # 'minimize' or 'maximize' the objective
+    direction: maximize
+
+    # total number of runs that will be executed
+    n_trials: 20
+
+    # choose Optuna hyperparameter sampler
+    # you can choose bayesian sampler (tpe), random search (without optimization), grid sampler, and others
+    # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html
+    sampler:
+      _target_: optuna.samplers.TPESampler
+      seed: 1234
+      n_startup_trials: 10 # number of random sampling runs before optimization starts
+
+    # define hyperparameter search space
+    params:
+      model.optimizer.lr: interval(0.0001, 0.1)
+      data.batch_size: choice(32, 64, 128, 256)
+      model.net.lin1_size: choice(64, 128, 256)
+      model.net.lin2_size: choice(64, 128, 256)
+      model.net.lin3_size: choice(32, 64, 128, 256)

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/hydra/default.yaml

+# https://hydra.cc/docs/configure_hydra/intro/
+
+# enable color logging
+defaults:
+  - override hydra_logging: colorlog
+  - override job_logging: colorlog
+
+# output directory, generated dynamically on each run
+run:
+  dir: ${paths.log_dir}/${task_name}/${run_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S}
+sweep:
+  dir: ${paths.log_dir}/${task_name}/${run_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S}
+  subdir: ${hydra.job.num}
+
+job_logging:
+  handlers:
+    file:
+      # Incorporates fix from https://github.com/facebookresearch/hydra/pull/2242
+      filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/aim.yaml

+# https://aimstack.io/
+
+# example usage in lightning module:
+# https://github.com/aimhubio/aim/blob/main/examples/pytorch_lightning_track.py
+
+# open the Aim UI with the following command (run in the folder containing the `.aim` folder):
+# `aim up`
+
+aim:
+  _target_: aim.pytorch_lightning.AimLogger
+  repo: ${paths.root_dir} # .aim folder will be created here
+  # repo: "aim://ip_address:port" # can instead provide IP address pointing to Aim remote tracking server which manages the repo, see https://aimstack.readthedocs.io/en/latest/using/remote_tracking.html#
+
+  # aim allows to group runs under experiment name
+  experiment: null # any string, set to "default" if not specified
+
+  train_metric_prefix: "train/"
+  val_metric_prefix: "val/"
+  test_metric_prefix: "test/"
+
+  # sets the tracking interval in seconds for system usage metrics (CPU, GPU, memory, etc.)
+  system_tracking_interval: 10 # set to null to disable system metrics tracking
+
+  # enable/disable logging of system params such as installed packages, git info, env vars, etc.
+  log_system_params: true
+
+  # enable/disable tracking console logs (default value is true)
+  capture_terminal_logs: false # set to false to avoid infinite console log loop issue https://github.com/aimhubio/aim/issues/2550

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/comet.yaml

+# https://www.comet.ml
+
+comet:
+  _target_: lightning.pytorch.loggers.comet.CometLogger
+  api_key: ${oc.env:COMET_API_TOKEN} # api key is loaded from environment variable
+  save_dir: "${paths.output_dir}"
+  project_name: "lightning-hydra-template"
+  rest_api_key: null
+  # experiment_name: ""
+  experiment_key: null # set to resume experiment
+  offline: False
+  prefix: ""

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/csv.yaml

+# csv logger built in lightning
+
+csv:
+  _target_: lightning.pytorch.loggers.csv_logs.CSVLogger
+  save_dir: "${paths.output_dir}"
+  name: "csv/"
+  prefix: ""

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/many_loggers.yaml

+# train with many loggers at once
+
+defaults:
+  # - comet
+  - csv
+  # - mlflow
+  # - neptune
+  - tensorboard
+  - wandb

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/mlflow.yaml

+# https://mlflow.org
+
+mlflow:
+  _target_: lightning.pytorch.loggers.mlflow.MLFlowLogger
+  # experiment_name: ""
+  # run_name: ""
+  tracking_uri: ${paths.log_dir}/mlflow/mlruns # run `mlflow ui` command inside the `logs/mlflow/` dir to open the UI
+  tags: null
+  # save_dir: "./mlruns"
+  prefix: ""
+  artifact_location: null
+  # run_id: ""

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/neptune.yaml

+# https://neptune.ai
+
+neptune:
+  _target_: lightning.pytorch.loggers.neptune.NeptuneLogger
+  api_key: ${oc.env:NEPTUNE_API_TOKEN} # api key is loaded from environment variable
+  project: username/lightning-hydra-template
+  # name: ""
+  log_model_checkpoints: True
+  prefix: ""

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/tensorboard.yaml

+# https://www.tensorflow.org/tensorboard/
+
+tensorboard:
+  _target_: lightning.pytorch.loggers.tensorboard.TensorBoardLogger
+  save_dir: "${paths.output_dir}/tensorboard/"
+  name: null
+  log_graph: False
+  default_hp_metric: True
+  prefix: ""
+  # version: ""

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/logger/wandb.yaml

+# https://wandb.ai
+
+wandb:
+  _target_: lightning.pytorch.loggers.wandb.WandbLogger
+  # name: "" # name of the run (normally generated by wandb)
+  save_dir: "${paths.output_dir}"
+  offline: False
+  id: null # pass correct id to resume experiment!
+  anonymous: null # enable anonymous logging
+  project: "lightning-hydra-template"
+  log_model: False # upload lightning ckpts
+  prefix: "" # a string to put at the beginning of metric keys
+  # entity: "" # set to name of your wandb team
+  group: ""
+  tags: []
+  job_type: ""

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/model/cfm/default.yaml

+name: CFM
+solver: euler
+sigma_min: 1e-4

third_party/GLM-4-Voice/third_party/Matcha-TTS/configs/model/decoder/default.yaml

+channels: [256, 256]
+dropout: 0.05
+attention_head_dim: 64
+n_blocks: 1
+num_mid_blocks: 2
+num_heads: 2
+act_fn: snakebeta