forked from coqui-ai/TTS
-
Notifications
You must be signed in to change notification settings - Fork 109
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
test(zoo): move big model tests into separate files
Makes it easier to run them locally: make test_zoo_big
- Loading branch information
Showing
3 changed files
with
196 additions
and
175 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,193 @@ | ||
| """These tests should be run locally because the models are too big for CI.""" | ||
|
|
||
| import os | ||
|
|
||
| import pytest | ||
| import torch | ||
|
|
||
| from tests import get_tests_data_path, run_main | ||
| from TTS.bin.synthesize import main | ||
| from TTS.utils.manage import ModelManager | ||
|
|
||
| GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true" | ||
|
|
||
|
|
||
| @pytest.fixture(scope="session", autouse=True) | ||
| def set_env(): | ||
| os.environ["COQUI_TOS_AGREED"] = "1" | ||
|
|
||
|
|
||
| @pytest.fixture | ||
| def manager(): | ||
| """Set up model manager.""" | ||
| return ModelManager(progress_bar=False) | ||
|
|
||
|
|
||
| @pytest.mark.skipif(GITHUB_ACTIONS, reason="Model too big for CI") | ||
| def test_xtts(tmp_path): | ||
| """XTTS is too big to run on github actions. We need to test it locally""" | ||
| args = [ | ||
| "--model_name", | ||
| "tts_models/multilingual/multi-dataset/xtts_v1.1", | ||
| "--text", | ||
| "C'est un exemple.", | ||
| "--language_idx", | ||
| "fr", | ||
| "--out_path", | ||
| str(tmp_path / "output.wav"), | ||
| "--no-progress_bar", | ||
| "--speaker_wav", | ||
| os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav"), | ||
| ] | ||
| if torch.cuda.is_available(): | ||
| args.append("--use_cuda") | ||
| run_main(main, args) | ||
|
|
||
|
|
||
| @pytest.mark.skipif(GITHUB_ACTIONS, reason="Model too big for CI") | ||
| def test_xtts_streaming(manager): | ||
| """Testing the new inference_stream method""" | ||
| from TTS.tts.configs.xtts_config import XttsConfig | ||
| from TTS.tts.models.xtts import Xtts | ||
|
|
||
| speaker_wav = [os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")] | ||
| speaker_wav_2 = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0002.wav") | ||
| speaker_wav.append(speaker_wav_2) | ||
| model_path, _, _ = manager.download_model("tts_models/multilingual/multi-dataset/xtts_v1.1") | ||
| config = XttsConfig() | ||
| config.load_json(model_path / "config.json") | ||
| model = Xtts.init_from_config(config) | ||
| model.load_checkpoint(config, checkpoint_dir=str(model_path)) | ||
| model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | ||
|
|
||
| print("Computing speaker latents...") | ||
| gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav) | ||
|
|
||
| print("Inference...") | ||
| chunks = model.inference_stream( | ||
| "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", | ||
| "en", | ||
| gpt_cond_latent, | ||
| speaker_embedding, | ||
| ) | ||
| wav_chunks = [] | ||
| for i, chunk in enumerate(chunks): | ||
| if i == 0: | ||
| assert chunk.shape[-1] > 5000 | ||
| wav_chunks.append(chunk) | ||
| assert len(wav_chunks) > 1 | ||
|
|
||
|
|
||
| @pytest.mark.skipif(GITHUB_ACTIONS, reason="Model too big for CI") | ||
| def test_xtts_v2(tmp_path): | ||
| """XTTS is too big to run on github actions. We need to test it locally""" | ||
| args = [ | ||
| "--model_name", | ||
| "tts_models/multilingual/multi-dataset/xtts_v2", | ||
| "--text", | ||
| "C'est un exemple.", | ||
| "--language_idx", | ||
| "fr", | ||
| "--out_path", | ||
| str(tmp_path / "output.wav"), | ||
| "--no-progress_bar", | ||
| "--speaker_wav", | ||
| os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav"), | ||
| os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0002.wav"), | ||
| ] | ||
| if torch.cuda.is_available(): | ||
| args.append("--use_cuda") | ||
| run_main(main, args) | ||
|
|
||
|
|
||
| @pytest.mark.skipif(GITHUB_ACTIONS, reason="Model too big for CI") | ||
| def test_xtts_v2_streaming(manager): | ||
| """Testing the new inference_stream method""" | ||
| from TTS.tts.configs.xtts_config import XttsConfig | ||
| from TTS.tts.models.xtts import Xtts | ||
|
|
||
| speaker_wav = [os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")] | ||
| model_path, _, _ = manager.download_model("tts_models/multilingual/multi-dataset/xtts_v2") | ||
| config = XttsConfig() | ||
| config.load_json(model_path / "config.json") | ||
| model = Xtts.init_from_config(config) | ||
| model.load_checkpoint(config, checkpoint_dir=str(model_path)) | ||
| model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | ||
|
|
||
| print("Computing speaker latents...") | ||
| gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav) | ||
|
|
||
| print("Inference...") | ||
| chunks = model.inference_stream( | ||
| "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", | ||
| "en", | ||
| gpt_cond_latent, | ||
| speaker_embedding, | ||
| ) | ||
| wav_chunks = [] | ||
| for i, chunk in enumerate(chunks): | ||
| if i == 0: | ||
| assert chunk.shape[-1] > 5000 | ||
| wav_chunks.append(chunk) | ||
| assert len(wav_chunks) > 1 | ||
| normal_len = sum([len(chunk) for chunk in wav_chunks]) | ||
|
|
||
| chunks = model.inference_stream( | ||
| "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", | ||
| "en", | ||
| gpt_cond_latent, | ||
| speaker_embedding, | ||
| speed=1.5, | ||
| ) | ||
| wav_chunks = [] | ||
| for i, chunk in enumerate(chunks): | ||
| wav_chunks.append(chunk) | ||
| fast_len = sum([len(chunk) for chunk in wav_chunks]) | ||
|
|
||
| chunks = model.inference_stream( | ||
| "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", | ||
| "en", | ||
| gpt_cond_latent, | ||
| speaker_embedding, | ||
| speed=0.66, | ||
| ) | ||
| wav_chunks = [] | ||
| for i, chunk in enumerate(chunks): | ||
| wav_chunks.append(chunk) | ||
| slow_len = sum([len(chunk) for chunk in wav_chunks]) | ||
|
|
||
| assert slow_len > normal_len | ||
| assert normal_len > fast_len | ||
|
|
||
|
|
||
| @pytest.mark.skipif(GITHUB_ACTIONS, reason="Model too big for CI") | ||
| def test_tortoise(tmp_path): | ||
| args = [ | ||
| "--model_name", | ||
| "tts_models/en/multi-dataset/tortoise-v2", | ||
| "--text", | ||
| "This is an example.", | ||
| "--out_path", | ||
| str(tmp_path / "output.wav"), | ||
| "--no-progress_bar", | ||
| ] | ||
| if torch.cuda.is_available(): | ||
| args.append("--use_cuda") | ||
| run_main(main, args) | ||
|
|
||
|
|
||
| @pytest.mark.skipif(GITHUB_ACTIONS, reason="Model too big for CI") | ||
| def test_bark(tmp_path): | ||
| """Bark is too big to run on github actions. We need to test it locally""" | ||
| args = [ | ||
| "--model_name", | ||
| "tts_models/multilingual/multi-dataset/bark", | ||
| "--text", | ||
| "This is an example.", | ||
| "--out_path", | ||
| str(tmp_path / "output.wav"), | ||
| "--no-progress_bar", | ||
| ] | ||
| if torch.cuda.is_available(): | ||
| args.append("--use_cuda") | ||
| run_main(main, args) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters