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refactoring tests after Coqpit

pull/476/head
Eren Gölge 2021-05-10 15:27:23 +02:00
parent 87384c6008
commit 5aee30443f
29 changed files with 156 additions and 153 deletions
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8
run_bash_tests.sh
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@ -2,13 +2,7 @@ set -e
TF_CPP_MIN_LOG_LEVEL=3
# runtime bash based tests
# TODO: move these to python
./tests/bash_tests/test_demo_server.sh && \
./tests/bash_tests/test_resample.sh && \
./tests/bash_tests/test_tacotron_train.sh && \
./tests/bash_tests/test_glow-tts_train.sh && \
./tests/bash_tests/test_vocoder_gan_train.sh && \
./tests/bash_tests/test_vocoder_wavernn_train.sh && \
./tests/bash_tests/test_vocoder_wavegrad_train.sh && \
./tests/bash_tests/test_speedy_speech_train.sh && \
./tests/bash_tests/test_aligntts_train.sh && \
./tests/bash_tests/test_compute_statistics.sh

52
tests/inputs/test_config.json
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@ -1,24 +1,24 @@
{
"audio":{
"audio_processor": "audio", // to use dictate different audio processors, if available.
"num_mels": 80, // size of the mel spec frame.
"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 22050, // wav sample-rate. If different than the original data, it is resampled.
"frame_length_ms": null, // stft window length in ms.
"frame_shift_ms": null, // stft window hop-lengh in ms.
"audio_processor": "audio",
"num_mels": 80,
"fft_size": 1024,
"sample_rate": 22050,
"frame_length_ms": null,
"frame_shift_ms": null,
"hop_length": 256,
"win_length": 1024,
"preemphasis": 0.97, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"min_level_db": -100, // normalization range
"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 30,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": true, // move normalization to range [-1, 1]
"clip_norm": true, // clip normalized values into the range.
"max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"mel_fmin": 0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 8000, // maximum freq level for mel-spec. Tune for dataset!!
"preemphasis": 0.97,
"min_level_db": -100,
"ref_level_db": 20,
"power": 1.5,
"griffin_lim_iters": 30,
"signal_norm": true,
"symmetric_norm": true,
"clip_norm": true,
"max_norm": 4,
"mel_fmin": 0,
"mel_fmax": 8000,
"do_trim_silence": false,
"spec_gain": 20
},
@ -53,15 +53,15 @@
"max_seq_len": 300,
"log_dir": "tests/outputs/",
// MULTI-SPEAKER and GST
"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
"use_gst": true, // use global style tokens
"gst": { // gst parameter if gst is enabled
"gst_style_input": null, // Condition the style input either on a
// -> wave file [path to wave] or
// -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
// with the dictionary being len(dict) <= len(gst_num_style_tokens).
"gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
"use_speaker_embedding": false,
"use_gst": true,
"gst": {
"gst_style_input": null,
"gst_use_speaker_embedding": true,
"gst_embedding_dim": 512,
"gst_num_heads": 4,
"gst_num_style_tokens": 10

4
tests/inputs/test_speaker_encoder_config.json
View File

@ -1,5 +1,6 @@
{
"model": "speaker_encoder",
"run_name": "test_speaker_encoder",
"run_description": "test speaker encoder.",
"audio":{
@ -42,8 +43,9 @@
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
"print_step": 20, // Number of steps to log traning on console.
"batch_size": 32,
"output_path": "", // DATASET-RELATED: output path for all training outputs.
"model": {
"model_params": {
"input_dim": 40,
"proj_dim": 256,
"lstm_dim": 768,

1
tests/outputs/dummy_model_config.json
View File

@ -87,7 +87,6 @@
// MULTI-SPEAKER and GST
"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
"use_gst": true, // use global style tokens
"gst": { // gst parameter if gst is enabled
"gst_style_input": null, // Condition the style input either on a
// -> wave file [path to wave] or

16
tests/test_audio.py
View File

@ -3,21 +3,21 @@ import unittest
from tests import get_tests_input_path, get_tests_output_path, get_tests_path
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config
from TTS.config import BaseAudioConfig
TESTS_PATH = get_tests_path()
OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
os.makedirs(OUT_PATH, exist_ok=True)
conf = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
conf = BaseAudioConfig(mel_fmax=8000)
# pylint: disable=protected-access
class TestAudio(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.ap = AudioProcessor(**conf.audio)
self.ap = AudioProcessor(**conf)
def test_audio_synthesis(self):
"""1. load wav
@ -163,12 +163,12 @@ class TestAudio(unittest.TestCase):
def test_scaler(self):
scaler_stats_path = os.path.join(get_tests_input_path(), "scale_stats.npy")
conf.audio["stats_path"] = scaler_stats_path
conf.audio["preemphasis"] = 0.0
conf.audio["do_trim_silence"] = True
conf.audio["signal_norm"] = True
conf.stats_path = scaler_stats_path
conf.preemphasis = 0.0
conf.do_trim_silence = True
conf.signal_norm = True
ap = AudioProcessor(**conf.audio)
ap = AudioProcessor(**conf)
mel_mean, mel_std, linear_mean, linear_std, _ = ap.load_stats(scaler_stats_path)
ap.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)

2
tests/test_glow_tts.py
View File

@ -17,7 +17,7 @@ torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
c = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
c = GlowTTSConfig()
ap = AudioProcessor(**c.audio)
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")

10
tests/test_loader.py
View File

@ -10,13 +10,17 @@ from tests import get_tests_input_path, get_tests_output_path
from TTS.tts.datasets import TTSDataset
from TTS.tts.datasets.preprocess import ljspeech
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config
from TTS.tts.configs import BaseTTSConfig
# pylint: disable=unused-variable
OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
os.makedirs(OUTPATH, exist_ok=True)
c = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
# create a dummy config for testing data loaders.
c = BaseTTSConfig(text_cleaner='english_cleaners', num_loader_workers=0, batch_size=2)
c.r = 5
c.data_path = "tests/data/ljspeech/"
ok_ljspeech = os.path.exists(c.data_path)
DATA_EXIST = True
@ -40,7 +44,7 @@ class TestTTSDataset(unittest.TestCase):
compute_linear_spec=True,
ap=self.ap,
meta_data=items,
tp=c.characters if "characters" in c.keys() else None,
tp=c.characters,
batch_group_size=bgs,
min_seq_len=c.min_seq_len,
max_seq_len=float("inf"),

11
tests/test_speaker_encoder_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.speaker_encoder.speaker_encoder_config import SpeakerEncoderConfig
from TTS.config.shared_configs import BaseAudioConfig
@ -15,9 +16,9 @@ config = SpeakerEncoderConfig(
num_speakers_in_batch=1,
num_utters_per_speaker=10,
num_loader_workers=0,
max_train_step=10,
max_train_step=2,
print_step=1,
save_step=10,
save_step=1,
print_eval=True,
audio=BaseAudioConfig(num_mels=40)
)
@ -27,7 +28,7 @@ config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_encoder.py --config_path {config_path} "
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.name ljspeech "
"--coqpit.datasets.0.meta_file_train metadata.csv "
@ -41,6 +42,6 @@ run_cli(command_train)
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_encoder.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

6
tests/test_speaker_manager.py
View File

@ -26,11 +26,11 @@ class SpeakerManagerTest(unittest.TestCase):
def test_speaker_embedding():
# load config
config = load_config(encoder_config_path)
config["audio"]["resample"] = True
config.audio.resample = True
# create a dummy speaker encoder
model = SpeakerEncoder(**config.model)
save_checkpoint(model, None, None, get_tests_input_path(), 0, 0)
model = SpeakerEncoder(**config.model_params)
save_checkpoint(model, None, None, get_tests_input_path(), 0)
# load audio processor and speaker encoder
ap = AudioProcessor(**config.audio)

3
tests/test_synthesize.py
View File

@ -1,6 +1,7 @@
import os
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
def test_synthesize():

11
tests/test_synthesizer.py
View File

@ -15,8 +15,8 @@ class SynthesizerTest(unittest.TestCase):
# pylint: disable=global-statement
global symbols, phonemes
config = load_config(os.path.join(get_tests_output_path(), "dummy_model_config.json"))
if "characters" in config.keys():
symbols, phonemes = make_symbols(**config.characters)
if config.has('characters') and config.characters:
symbols, phonemes = make_symbols(**config.characters.to_dict())
num_chars = len(phonemes) if config.use_phonemes else len(symbols)
model = setup_model(num_chars, 0, config)
@ -25,11 +25,10 @@ class SynthesizerTest(unittest.TestCase):
def test_in_out(self):
self._create_random_model()
config = load_config(os.path.join(get_tests_input_path(), "server_config.json"))
tts_root_path = get_tests_output_path()
config["tts_checkpoint"] = os.path.join(tts_root_path, config["tts_checkpoint"])
config["tts_config"] = os.path.join(tts_root_path, config["tts_config"])
synthesizer = Synthesizer(config["tts_checkpoint"], config["tts_config"], None, None)
tts_checkpoint = os.path.join(tts_root_path, 'checkpoint_10.pth.tar')
tts_config = os.path.join(tts_root_path, 'dummy_model_config.json')
synthesizer = Synthesizer(tts_checkpoint, tts_config, None, None)
synthesizer.tts("Better this test works!!")
def test_split_into_sentences(self):

21
tests/test_tacotron2_model.py
View File

@ -17,7 +17,7 @@ torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
c = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
c = Tacotron2Config()
ap = AudioProcessor(**c.audio)
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
@ -152,10 +152,8 @@ class TacotronGSTTrainTest(unittest.TestCase):
num_chars=24,
r=c.r,
num_speakers=5,
gst=True,
gst_embedding_dim=c.gst["gst_embedding_dim"],
gst_num_heads=c.gst["gst_num_heads"],
gst_style_tokens=c.gst["gst_style_tokens"],
use_gst=True,
gst=c.gst
).to(device)
model.train()
model_ref = copy.deepcopy(model)
@ -216,10 +214,8 @@ class TacotronGSTTrainTest(unittest.TestCase):
num_chars=24,
r=c.r,
num_speakers=5,
gst=True,
gst_embedding_dim=c.gst["gst_embedding_dim"],
gst_num_heads=c.gst["gst_num_heads"],
gst_style_tokens=c.gst["gst_style_tokens"],
use_gst=True,
gst =c.gst
).to(device)
model.train()
model_ref = copy.deepcopy(model)
@ -280,11 +276,8 @@ class SCGSTMultiSpeakeTacotronTrainTest(unittest.TestCase):
r=c.r,
num_speakers=5,
speaker_embedding_dim=55,
gst=True,
gst_embedding_dim=c.gst["gst_embedding_dim"],
gst_num_heads=c.gst["gst_num_heads"],
gst_style_tokens=c.gst["gst_style_tokens"],
gst_use_speaker_embedding=c.gst["gst_use_speaker_embedding"],
use_gst=True,
gst=c.gst
).to(device)
model.train()
model_ref = copy.deepcopy(model)

2
tests/test_tacotron2_tf_model.py
View File

@ -19,7 +19,7 @@ torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
c = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
c = Tacotron2Config()
class TacotronTFTrainTest(unittest.TestCase):

21
tests/test_tacotron_model.py
View File

@ -18,7 +18,7 @@ torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
c = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
c = TacotronConfig()
ap = AudioProcessor(**c.audio)
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
@ -175,10 +175,8 @@ class TacotronGSTTrainTest(unittest.TestCase):
model = Tacotron(
num_chars=32,
num_speakers=5,
gst=True,
gst_embedding_dim=c.gst["gst_embedding_dim"],
gst_num_heads=c.gst["gst_num_heads"],
gst_style_tokens=c.gst["gst_style_tokens"],
use_gst=True,
gst=c.gst,
postnet_output_dim=c.audio["fft_size"],
decoder_output_dim=c.audio["num_mels"],
r=c.r,
@ -240,10 +238,8 @@ class TacotronGSTTrainTest(unittest.TestCase):
model = Tacotron(
num_chars=32,
num_speakers=5,
gst=True,
gst_embedding_dim=c.gst["gst_embedding_dim"],
gst_num_heads=c.gst["gst_num_heads"],
gst_style_tokens=c.gst["gst_style_tokens"],
use_gst=True,
gst=c.gst,
postnet_output_dim=c.audio["fft_size"],
decoder_output_dim=c.audio["num_mels"],
r=c.r,
@ -306,11 +302,8 @@ class SCGSTMultiSpeakeTacotronTrainTest(unittest.TestCase):
num_speakers=5,
postnet_output_dim=c.audio["fft_size"],
decoder_output_dim=c.audio["num_mels"],
gst=True,
gst_embedding_dim=c.gst["gst_embedding_dim"],
gst_num_heads=c.gst["gst_num_heads"],
gst_style_tokens=c.gst["gst_style_tokens"],
gst_use_speaker_embedding=c.gst["gst_use_speaker_embedding"],
use_gst=True,
gst=c.gst,
r=c.r,
memory_size=c.memory_size,
speaker_embedding_dim=55,

7
tests/tts_tests/test_align_tts_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.tts.configs import AlignTTSConfig
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
@ -30,7 +31,7 @@ config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_align_tts.py --config_path {config_path} "
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_align_tts.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.name ljspeech "
"--coqpit.datasets.0.meta_file_train metadata.csv "
@ -43,6 +44,6 @@ run_cli(command_train)
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_align_tts.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_align_tts.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/tts_tests/test_glow_tts_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.tts.configs import GlowTTSConfig
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
@ -30,7 +31,7 @@ config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_glow_tts.py --config_path {config_path} "
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_glow_tts.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.name ljspeech "
"--coqpit.datasets.0.meta_file_train metadata.csv "
@ -44,6 +45,6 @@ run_cli(command_train)
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_glow_tts.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_glow_tts.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/tts_tests/test_speedy_speech_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.tts.configs import SpeedySpeechConfig
config_path = os.path.join(get_tests_output_path(), "test_speedy_speech_config.json")
@ -30,7 +31,7 @@ config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_speedy_speech.py --config_path {config_path} "
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_speedy_speech.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.name ljspeech "
"--coqpit.datasets.0.meta_file_train metadata.csv "
@ -44,6 +45,6 @@ run_cli(command_train)
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_speedy_speech.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_speedy_speech.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/tts_tests/test_tacotron2_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.tts.configs import Tacotron2Config
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
@ -31,7 +32,7 @@ config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_tacotron.py --config_path {config_path} "
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.name ljspeech "
"--coqpit.datasets.0.meta_file_train metadata.csv "
@ -44,6 +45,6 @@ run_cli(command_train)
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_tacotron.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/tts_tests/test_tacotron_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.tts.configs import TacotronConfig
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
@ -30,7 +31,7 @@ config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_tacotron.py --config_path {config_path} "
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.name ljspeech "
"--coqpit.datasets.0.meta_file_train metadata.csv "
@ -43,6 +44,6 @@ run_cli(command_train)
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_tacotron.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/vocoder_tests/test_fullband_melgan_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.vocoder.configs import FullbandMelganConfig
config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
@ -28,13 +29,13 @@ config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/vocoder_tests/test_hifigan_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.vocoder.configs import HifiganConfig
config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
@ -29,13 +30,13 @@ config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/vocoder_tests/test_melgan_train.py
View File

@ -2,9 +2,10 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.vocoder.configs import MelganConfig
config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")
@ -28,13 +29,13 @@ config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/vocoder_tests/test_multiband_melgan_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.vocoder.configs import MultibandMelganConfig
config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
@ -28,13 +29,13 @@ config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

7
tests/vocoder_tests/test_parallel_wavegan_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.vocoder.configs import ParallelWaveganConfig
config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
@ -28,13 +29,13 @@ config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --config_path {config_path} "
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_gan.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

11
tests/vocoder_tests/test_vocoder_gan_datasets.py
View File

@ -3,17 +3,17 @@ import os
import numpy as np
from torch.utils.data import DataLoader
from tests import get_tests_input_path, get_tests_output_path, get_tests_path
from tests import get_tests_output_path, get_tests_path
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config
from TTS.vocoder.datasets.gan_dataset import GANDataset
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.configs import BaseGANVocoderConfig
file_path = os.path.dirname(os.path.realpath(__file__))
OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
os.makedirs(OUTPATH, exist_ok=True)
C = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
C = BaseGANVocoderConfig()
test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
ok_ljspeech = os.path.exists(test_data_path)
@ -46,6 +46,8 @@ def gan_dataset_case(
def check_item(feat, wav):
"""Pass a single pair of features and waveform"""
feat = feat.numpy()
wav = wav.numpy()
expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
# check shapes
@ -61,7 +63,7 @@ def gan_dataset_case(
# the first 2 and the last 2 frames are skipped due to the padding
# differences in stft
max_diff = abs((feat - mel[:, : feat.shape[-1]])[:, 2:-2]).max()
assert max_diff <= 0, f" [!] {max_diff}"
assert max_diff <= 1e-6, f" [!] {max_diff}"
# return random segments or return the whole audio
if return_segments:
@ -69,7 +71,6 @@ def gan_dataset_case(
for item1, item2 in loader:
feat1, wav1 = item1
feat2, wav2 = item2
check_item(feat1, wav1)
check_item(feat2, wav2)
count_iter += 1

3
tests/vocoder_tests/test_vocoder_losses.py
View File

@ -14,8 +14,7 @@ os.makedirs(OUT_PATH, exist_ok=True)
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
C = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
ap = AudioProcessor(**C.audio)
ap = AudioProcessor(**BaseAudioConfig().to_dict())
def test_torch_stft():

2
tests/vocoder_tests/test_vocoder_wavernn_datasets.py
View File

@ -14,7 +14,7 @@ file_path = os.path.dirname(os.path.realpath(__file__))
OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
os.makedirs(OUTPATH, exist_ok=True)
C = load_config(os.path.join(get_tests_input_path(), "test_vocoder_wavernn_config.json"))
C = WavernnConfig()
test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
test_mel_feat_path = os.path.join(test_data_path, "mel")

43
tests/vocoder_tests/test_wavegrad_train.py
View File

@ -2,39 +2,44 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.vocoder.configs import WavegradConfig
config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")
config = WavegradConfig(
batch_size=8,
eval_batch_size=8,
num_loader_workers=0,
num_val_loader_workers=0,
run_eval=True,
test_delay_epochs=-1,
epochs=1,
seq_len=8192,
eval_split_size=1,
print_step=1,
print_eval=True,
data_path="tests/data/ljspeech",
output_path=output_path,
)
config = WavegradConfig(batch_size=8,
eval_batch_size=8,
num_loader_workers=0,
num_val_loader_workers=0,
run_eval=True,
test_delay_epochs=-1,
epochs=1,
seq_len=8192,
eval_split_size=1,
print_step=1,
print_eval=True,
data_path="tests/data/ljspeech",
output_path=output_path,
test_noise_schedule={
"min_val": 1e-6,
"max_val": 1e-2,
"num_steps": 2
})
config.audio.do_trim_silence = True
config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_wavegrad.py --config_path {config_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_wavegrad.py --config_path {config_path} "
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
continue_path = max(glob.glob(os.path.join(output_path, "*/")),
key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_wavegrad.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_wavegrad.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)

12
tests/vocoder_tests/test_wavernn_train.py
View File

@ -2,7 +2,8 @@ import glob
import os
import shutil
from tests import get_tests_output_path, run_cli
from tests import get_tests_output_path, run_cli, get_device_id
from TTS.vocoder.configs import WavernnConfig
config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
@ -16,7 +17,7 @@ config = WavernnConfig(
run_eval=True,
test_delay_epochs=-1,
epochs=1,
seq_len=8192,
seq_len=256, # for shorter test time
eval_split_size=1,
print_step=1,
print_eval=True,
@ -28,13 +29,14 @@ config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_wavernn.py --config_path {config_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_wavernn.py --config_path {config_path} "
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
continue_path = max(glob.glob(os.path.join(output_path, "*/")),
key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_vocoder_wavernn.py --continue_path {continue_path} "
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder_wavernn.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)