Remove old unneeded files
This commit is contained in:
Josh Meyer
parent
9f7fda14cb
commit
ea82ab4cb8
4
start.sh
4
start.sh
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#!/usr/bin/env bash
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set -eu
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jupyter lab --ip=0.0.0.0 --port=8080 --allow-root
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16
tfenv.yml
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tfenv.yml
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name: tf1
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channels:
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- conda-forge
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dependencies:
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- python=3.7
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- tensorflow-gpu==1.15
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- ipykernel
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- google-auth
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- tensorflow-hub
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- pydicom
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- pandas
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- seaborn
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- matplotlib
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- scikit-learn
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- openslide
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- keras
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221
train-ldc.ipynb
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train-ldc.ipynb
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# Download LDC data
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import os
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import sys
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import pandas
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from STT.training.coqui_stt_training.util.downloader import maybe_download
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#from STT.bin.import_ldc93s1 import _download_and_preprocess_data as download_data
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#download_data('/home/STT/data')
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def download_and_preprocess_data(data_dir):
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# Conditionally download data
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LDC93S1_BASE = "LDC93S1"
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LDC93S1_BASE_URL = "https://catalog.ldc.upenn.edu/desc/addenda/"
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local_file = maybe_download(
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LDC93S1_BASE + ".wav", data_dir, LDC93S1_BASE_URL + LDC93S1_BASE + ".wav"
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)
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trans_file = maybe_download(
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LDC93S1_BASE + ".txt", data_dir, LDC93S1_BASE_URL + LDC93S1_BASE + ".txt"
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)
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with open(trans_file, "r") as fin:
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transcript = " ".join(fin.read().strip().lower().split(" ")[2:]).replace(
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".", ""
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)
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df = pandas.DataFrame(
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data=[(os.path.abspath(local_file), os.path.getsize(local_file), transcript)],
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columns=["wav_filename", "wav_filesize", "transcript"],
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)
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df.to_csv(os.path.join(data_dir, "ldc93s1.csv"), index=False)
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download_and_preprocess_data('/home/STT/data')
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# Train
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from STT.training.coqui_stt_training.util.config import _SttConfig, _ConfigSingleton
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from STT.training.coqui_stt_training.util.augmentations import parse_augmentations, NormalizeSampleRate
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from STT.training.coqui_stt_training.util.helpers import parse_file_size
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from STT.training.coqui_stt_training.util.gpu import get_available_gpus
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from coqui_stt_ctcdecoder import Alphabet
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from xdg import BaseDirectory as xdg
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import tensorflow.compat.v1 as tfv1
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def initialize_globals(c):
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# Augmentations
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c.augmentations = parse_augmentations(c.augment)
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print(f"Parsed augmentations from flags: {c.augmentations}")
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if c.augmentations and c.feature_cache and c.cache_for_epochs == 0:
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print(
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"Due to current feature-cache settings the exact same sample augmentations of the first "
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"epoch will be repeated on all following epochs. This could lead to unintended over-fitting. "
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"You could use --cache_for_epochs <n_epochs> to invalidate the cache after a given number of epochs."
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)
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if c.normalize_sample_rate:
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c.augmentations = [NormalizeSampleRate(c.audio_sample_rate)] + c[
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"augmentations"
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]
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# Caching
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if c.cache_for_epochs == 1:
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print(
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"--cache_for_epochs == 1 is (re-)creating the feature cache on every epoch but will never use it."
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)
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# Read-buffer
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c.read_buffer = parse_file_size(c.read_buffer)
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# Set default dropout rates
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if c.dropout_rate2 < 0:
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c.dropout_rate2 = c.dropout_rate
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if c.dropout_rate3 < 0:
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c.dropout_rate3 = c.dropout_rate
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if c.dropout_rate6 < 0:
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c.dropout_rate6 = c.dropout_rate
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# Set default checkpoint dir
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if not c.checkpoint_dir:
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c.checkpoint_dir = xdg.save_data_path(os.path.join("stt", "checkpoints"))
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if c.load_train not in ["last", "best", "init", "auto"]:
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c.load_train = "auto"
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if c.load_evaluate not in ["last", "best", "auto"]:
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c.load_evaluate = "auto"
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# Set default summary dir
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if not c.summary_dir:
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c.summary_dir = xdg.save_data_path(os.path.join("stt", "summaries"))
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# Standard session configuration that'll be used for all new sessions.
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c.session_config = tfv1.ConfigProto(
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allow_soft_placement=True,
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log_device_placement=c.log_placement,
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inter_op_parallelism_threads=c.inter_op_parallelism_threads,
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intra_op_parallelism_threads=c.intra_op_parallelism_threads,
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gpu_options=tfv1.GPUOptions(allow_growth=c.use_allow_growth),
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)
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# CPU device
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c.cpu_device = "/cpu:0"
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# Available GPU devices
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c.available_devices = get_available_gpus(c.session_config)
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# If there is no GPU available, we fall back to CPU based operation
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if not c.available_devices:
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c.available_devices = [c.cpu_device]
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c.alphabet_config_path=""
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if c.bytes_output_mode:
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c.alphabet = UTF8Alphabet()
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elif c.alphabet_config_path:
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c.alphabet = Alphabet(os.path.abspath(c.alphabet_config_path))
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# Geometric Constants
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# ===================
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# For an explanation of the meaning of the geometric constants, please refer to
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# doc/Geometry.md
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# Number of MFCC features
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c.n_input = 26 # TODO: Determine this programmatically from the sample rate
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# The number of frames in the context
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c.n_context = 9 # TODO: Determine the optimal value using a validation data set
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# Number of units in hidden layers
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c.n_hidden = c.n_hidden
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c.n_hidden_1 = c.n_hidden
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c.n_hidden_2 = c.n_hidden
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c.n_hidden_5 = c.n_hidden
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# LSTM cell state dimension
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c.n_cell_dim = c.n_hidden
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# The number of units in the third layer, which feeds in to the LSTM
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c.n_hidden_3 = c.n_cell_dim
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# Units in the sixth layer = number of characters in the target language plus one
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try:
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c.n_hidden_6 = c.alphabet.GetSize() + 1 # +1 for CTC blank label
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except:
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AttributeError
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# Size of audio window in samples
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if (c.feature_win_len * c.audio_sample_rate) % 1000 != 0:
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log_error(
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"--feature_win_len value ({}) in milliseconds ({}) multiplied "
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"by --audio_sample_rate value ({}) must be an integer value. Adjust "
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"your --feature_win_len value or resample your audio accordingly."
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"".format(c.feature_win_len, c.feature_win_len / 1000, c.audio_sample_rate)
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)
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sys.exit(1)
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c.audio_window_samples = c.audio_sample_rate * (c.feature_win_len / 1000)
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# Stride for feature computations in samples
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if (c.feature_win_step * c.audio_sample_rate) % 1000 != 0:
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log_error(
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"--feature_win_step value ({}) in milliseconds ({}) multiplied "
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"by --audio_sample_rate value ({}) must be an integer value. Adjust "
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"your --feature_win_step value or resample your audio accordingly."
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"".format(
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c.feature_win_step, c.feature_win_step / 1000, c.audio_sample_rate
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)
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)
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sys.exit(1)
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c.audio_step_samples = c.audio_sample_rate * (c.feature_win_step / 1000)
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if c.one_shot_infer:
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if not path_exists_remote(c.one_shot_infer):
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log_error("Path specified in --one_shot_infer is not a valid file.")
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sys.exit(1)
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if c.train_cudnn and c.load_cudnn:
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log_error(
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"Trying to use --train_cudnn, but --load_cudnn "
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"was also specified. The --load_cudnn flag is only "
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"needed when converting a CuDNN RNN checkpoint to "
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"a CPU-capable graph. If your system is capable of "
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"using CuDNN RNN, you can just specify the CuDNN RNN "
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"checkpoint normally with --save_checkpoint_dir."
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)
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sys.exit(1)
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# If separate save and load flags were not specified, default to load and save
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# from the same dir.
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if not c.save_checkpoint_dir:
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c.save_checkpoint_dir = c.checkpoint_dir
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if not c.load_checkpoint_dir:
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c.load_checkpoint_dir = c.checkpoint_dir
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_ConfigSingleton._config = c # pylint: disable=protected-access
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from STT.training.coqui_stt_training.train import train, test, early_training_checks
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Config = _SttConfig()
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Config.alphabet = Alphabet('/home/STT/data/alphabet.txt')
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Config.train_files=['/home/STT/data/ldc93s1.csv']
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Config.dev_files=['/home/STT/data/ldc93s1.csv']
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Config.test_files=['/home/STT/data/ldc93s1.csv']
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Config.n_hidden=100
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Config.epochs=200
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initialize_globals(Config)
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#print(Config.to_json())
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early_training_checks()
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train()
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tfv1.reset_default_graph()
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test()
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