Add unit tests

TTS/tts/models/vits.py

@@ -632,7 +632,10 @@ class Vits(BaseTTS):
         )
 
         if self.args.init_discriminator:
-            self.disc = VitsDiscriminator(periods=self.args.periods_multi_period_discriminator, use_spectral_norm=self.args.use_spectral_norm_disriminator)
+            self.disc = VitsDiscriminator(
+                periods=self.args.periods_multi_period_discriminator,
+                use_spectral_norm=self.args.use_spectral_norm_disriminator,
+            )
 
         if self.args.TTS_part_sample_rate:
             self.interpolate_factor = self.config.audio["sample_rate"] / self.args.TTS_part_sample_rate

tests/tts_tests/test_vits_multilingual_speaker_emb_train.py

@@ -71,14 +71,6 @@ config.use_sdp = False
 # active language sampler
 config.use_language_weighted_sampler = True
 
-# test upsample
-config.model_args.TTS_part_sample_rate = 11025
-config.model_args.interpolate_z = False
-config.model_args.detach_z_vocoder = True
-
-config.model_args.upsample_rates_decoder = [8, 8, 4, 2]
-config.model_args.periods_multi_period_discriminator = [2, 3, 5, 7, 11, 13, 17, 19, 23]
-
 config.save_json(config_path)
 
 # train the model for one epoch

tests/tts_tests/test_vits_speaker_emb_train_upsampling_interpolation_approach.py

@@ -0,0 +1,90 @@
+import glob
+import json
+import os
+import shutil
+
+from trainer import get_last_checkpoint
+
+from tests import get_device_id, get_tests_output_path, run_cli
+from TTS.tts.configs.vits_config import VitsConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+
+config = VitsConfig(
+    batch_size=2,
+    eval_batch_size=2,
+    num_loader_workers=0,
+    num_eval_loader_workers=0,
+    text_cleaner="english_cleaners",
+    use_phonemes=True,
+    phoneme_language="en-us",
+    phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    print_step=1,
+    print_eval=True,
+    test_sentences=[
+        ["Be a voice, not an echo.", "ljspeech-1"],
+    ],
+)
+# set audio config
+config.audio.do_trim_silence = True
+config.audio.trim_db = 60
+
+# active multispeaker d-vec mode
+config.model_args.use_speaker_embedding = True
+config.model_args.use_d_vector_file = False
+config.model_args.d_vector_file = None
+config.model_args.d_vector_dim = 256
+
+
+# test upsample interpolation approach
+config.model_args.TTS_part_sample_rate = 11025
+config.model_args.interpolate_z = True
+config.model_args.upsample_rates_decoder = [8, 8, 2, 2]
+config.model_args.periods_multi_period_discriminator = [2, 3, 5, 7]
+
+
+config.save_json(config_path)
+
+# train the model for one epoch
+command_train = (
+    f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
+    f"--coqpit.output_path {output_path} "
+    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.meta_file_train metadata.csv "
+    "--coqpit.datasets.0.meta_file_val metadata.csv "
+    "--coqpit.datasets.0.path tests/data/ljspeech "
+    "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
+    "--coqpit.test_delay_epochs 0"
+)
+run_cli(command_train)
+
+# Find latest folder
+continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
+
+# Inference using TTS API
+continue_config_path = os.path.join(continue_path, "config.json")
+continue_restore_path, _ = get_last_checkpoint(continue_path)
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
+speaker_id = "ljspeech-1"
+continue_speakers_path = os.path.join(continue_path, "speakers.json")
+
+# Check integrity of the config
+with open(continue_config_path, "r", encoding="utf-8") as f:
+    config_loaded = json.load(f)
+assert config_loaded["characters"] is not None
+assert config_loaded["output_path"] in continue_path
+assert config_loaded["test_delay_epochs"] == 0
+
+# Load the model and run inference
+inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
+run_cli(inference_command)
+
+# restore the model and continue training for one more epoch
+command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)

tests/tts_tests/test_vits_speaker_emb_train_upsampling_vocoder_approach.py

@@ -0,0 +1,90 @@
+import glob
+import json
+import os
+import shutil
+
+from trainer import get_last_checkpoint
+
+from tests import get_device_id, get_tests_output_path, run_cli
+from TTS.tts.configs.vits_config import VitsConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+
+config = VitsConfig(
+    batch_size=2,
+    eval_batch_size=2,
+    num_loader_workers=0,
+    num_eval_loader_workers=0,
+    text_cleaner="english_cleaners",
+    use_phonemes=True,
+    phoneme_language="en-us",
+    phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    print_step=1,
+    print_eval=True,
+    test_sentences=[
+        ["Be a voice, not an echo.", "ljspeech-1"],
+    ],
+)
+# set audio config
+config.audio.do_trim_silence = True
+config.audio.trim_db = 60
+
+# active multispeaker d-vec mode
+config.model_args.use_speaker_embedding = True
+config.model_args.use_d_vector_file = False
+config.model_args.d_vector_file = None
+config.model_args.d_vector_dim = 256
+
+
+# test upsample
+config.model_args.TTS_part_sample_rate = 11025
+config.model_args.interpolate_z = False
+config.model_args.upsample_rates_decoder = [8, 8, 4, 2]
+config.model_args.periods_multi_period_discriminator = [2, 3, 5, 7]
+
+
+config.save_json(config_path)
+
+# train the model for one epoch
+command_train = (
+    f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
+    f"--coqpit.output_path {output_path} "
+    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.meta_file_train metadata.csv "
+    "--coqpit.datasets.0.meta_file_val metadata.csv "
+    "--coqpit.datasets.0.path tests/data/ljspeech "
+    "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
+    "--coqpit.test_delay_epochs 0"
+)
+run_cli(command_train)
+
+# Find latest folder
+continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
+
+# Inference using TTS API
+continue_config_path = os.path.join(continue_path, "config.json")
+continue_restore_path, _ = get_last_checkpoint(continue_path)
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
+speaker_id = "ljspeech-1"
+continue_speakers_path = os.path.join(continue_path, "speakers.json")
+
+# Check integrity of the config
+with open(continue_config_path, "r", encoding="utf-8") as f:
+    config_loaded = json.load(f)
+assert config_loaded["characters"] is not None
+assert config_loaded["output_path"] in continue_path
+assert config_loaded["test_delay_epochs"] == 0
+
+# Load the model and run inference
+inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
+run_cli(inference_command)
+
+# restore the model and continue training for one more epoch
+command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)