Python API implementation

.github/workflows/aux_tests.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

.github/workflows/data_tests.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

.github/workflows/inference_tests.yml

@@ -31,10 +31,13 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update
  sudo apt-get install -y --no-install-recommends git make gcc
+ sudo apt-get install espeak-ng
  make system-deps
  - name: Install/upgrade Python setup deps
  run: python3 -m pip install --upgrade pip setuptools wheel

.github/workflows/text_tests.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

.github/workflows/tts_tests.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

.github/workflows/vocoder_tests.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

.github/workflows/zoo_tests0.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

.github/workflows/zoo_tests1.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

.github/workflows/zoo_tests2.yml

@@ -31,6 +31,8 @@ jobs:
  cache-dependency-path: 'requirements*'
  - name: check OS
  run: cat /etc/os-release
+ - name: set ENV
+ run: export TRAINER_TELEMETRY=0
  - name: Install dependencies
  run: |
  sudo apt-get update

TTS/api.py

@@ -0,0 +1,146 @@
+from pathlib import Path
+
+from TTS.utils.manage import ModelManager
+from TTS.utils.synthesizer import Synthesizer
+
+
+class TTS:
+ """TODO: Add voice conversion and Capacitron support."""
+
+ def __init__(self, model_name: str = None, progress_bar: bool = True, gpu=False):
+ """🐸TTS python interface that allows to load and use the released models.
+
+ Example with a multi-speaker model:
+ >>> from TTS.api import TTS
+ >>> tts = TTS(TTS.list_models()[0])
+ >>> wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
+ >>> tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")
+
+ Example with a single-speaker model:
+ >>> tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
+ >>> tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path="output.wav")
+
+ Args:
+ model_name (str, optional): Model name to load. You can list models by ```tts.models```. Defaults to None.
+ progress_bar (bool, optional): Whether to pring a progress bar while downloading a model. Defaults to True.
+ gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
+ """
+ self.manager = ModelManager(models_file=self.get_models_file_path(), progress_bar=progress_bar, verbose=False)
+ self.synthesizer = None
+ if model_name:
+ self.load_model_by_name(model_name, gpu)
+
+ @property
+ def models(self):
+ return self.manager.list_tts_models()
+
+ @property
+ def is_multi_speaker(self):
+ if hasattr(self.synthesizer.tts_model, "speaker_manager") and self.synthesizer.tts_model.speaker_manager:
+ return self.synthesizer.tts_model.speaker_manager.num_speakers > 1
+ return False
+
+ @property
+ def is_multi_lingual(self):
+ if hasattr(self.synthesizer.tts_model, "language_manager") and self.synthesizer.tts_model.language_manager:
+ return self.synthesizer.tts_model.language_manager.num_languages > 1
+ return False
+
+ @property
+ def speakers(self):
+ if not self.is_multi_speaker:
+ return None
+ return self.synthesizer.tts_model.speaker_manager.speaker_names
+
+ @property
+ def languages(self):
+ if not self.is_multi_lingual:
+ return None
+ return self.synthesizer.tts_model.language_manager.language_names
+
+ @staticmethod
+ def get_models_file_path():
+ return Path(__file__).parent / ".models.json"
+
+ @staticmethod
+ def list_models():
+ manager = ModelManager(models_file=TTS.get_models_file_path(), progress_bar=False, verbose=False)
+ return manager.list_tts_models()
+
+ def download_model_by_name(self, model_name: str):
+ model_path, config_path, model_item = self.manager.download_model(model_name)
+ if model_item["default_vocoder"] is None:
+ return model_path, config_path, None, None
+ vocoder_path, vocoder_config_path, _ = self.manager.download_model(model_item["default_vocoder"])
+ return model_path, config_path, vocoder_path, vocoder_config_path
+
+ def load_model_by_name(self, model_name: str, gpu: bool = False):
+ model_path, config_path, vocoder_path, vocoder_config_path = self.download_model_by_name(model_name)
+ # init synthesizer
+ # None values are fetch from the model
+ self.synthesizer = Synthesizer(
+ tts_checkpoint=model_path,
+ tts_config_path=config_path,
+ tts_speakers_file=None,
+ tts_languages_file=None,
+ vocoder_checkpoint=vocoder_path,
+ vocoder_config=vocoder_config_path,
+ encoder_checkpoint=None,
+ encoder_config=None,
+ use_cuda=gpu,
+ )
+
+ def _check_arguments(self, speaker: str = None, language: str = None):
+ if self.is_multi_speaker and speaker is None:
+ raise ValueError("Model is multi-speaker but no speaker is provided.")
+ if self.is_multi_lingual and language is None:
+ raise ValueError("Model is multi-lingual but no language is provided.")
+ if not self.is_multi_speaker and speaker is not None:
+ raise ValueError("Model is not multi-speaker but speaker is provided.")
+ if not self.is_multi_lingual and language is not None:
+ raise ValueError("Model is not multi-lingual but language is provided.")
+
+ def tts(self, text: str, speaker: str = None, language: str = None):
+ """Convert text to speech.
+
+ Args:
+ text (str):
+ Input text to synthesize.
+ speaker (str, optional):
+ Speaker name for multi-speaker. You can check whether loaded model is multi-speaker by
+ `tts.is_multi_speaker` and list speakers by `tts.speakers`. Defaults to None.
+ language (str, optional):
+ Language code for multi-lingual models. You can check whether loaded model is multi-lingual
+ `tts.is_multi_lingual` and list available languages by `tts.languages`. Defaults to None.
+ """
+ self._check_arguments(speaker=speaker, language=language)
+
+ wav = self.synthesizer.tts(
+ text=text,
+ speaker_name=speaker,
+ language_name=language,
+ speaker_wav=None,
+ reference_wav=None,
+ style_wav=None,
+ style_text=None,
+ reference_speaker_name=None,
+ )
+ return wav
+
+ def tts_to_file(self, text: str, speaker: str = None, language: str = None, file_path: str = "output.wav"):
+ """Convert text to speech.
+
+ Args:
+ text (str):
+ Input text to synthesize.
+ speaker (str, optional):
+ Speaker name for multi-speaker. You can check whether loaded model is multi-speaker by
+ `tts.is_multi_speaker` and list speakers by `tts.speakers`. Defaults to None.
+ language (str, optional):
+ Language code for multi-lingual models. You can check whether loaded model is multi-lingual
+ `tts.is_multi_lingual` and list available languages by `tts.languages`. Defaults to None.
+ file_path (str, optional):
+ Output file path. Defaults to "output.wav".
+ """
+ wav = self.tts(text=text, speaker=speaker, language=language)
+ self.synthesizer.save_wav(wav=wav, path=file_path)

TTS/utils/manage.py

@@ -35,11 +35,13 @@ class ModelManager(object):
  models_file (str): path to .model.json file. Defaults to None.
  output_prefix (str): prefix to `tts` to download models. Defaults to None
  progress_bar (bool): print a progress bar when donwloading a file. Defaults to False.
+ verbose (bool): print info. Defaults to True.
  """
 
- def __init__(self, models_file=None, output_prefix=None, progress_bar=False):
+ def __init__(self, models_file=None, output_prefix=None, progress_bar=False, verbose=True):
  super().__init__()
  self.progress_bar = progress_bar
+ self.verbose = verbose
  if output_prefix is None:
  self.output_prefix = get_user_data_dir("tts")
  else:
@@ -62,30 +64,31 @@ def read_models_file(self, file_path):
  self.models_dict = json.load(json_file)
 
  def _list_models(self, model_type, model_count=0):
+ if self.verbose:
+ print(" Name format: type/language/dataset/model")
  model_list = []
  for lang in self.models_dict[model_type]:
  for dataset in self.models_dict[model_type][lang]:
  for model in self.models_dict[model_type][lang][dataset]:
  model_full_name = f"{model_type}--{lang}--{dataset}--{model}"
  output_path = os.path.join(self.output_prefix, model_full_name)
- if os.path.exists(output_path):
- print(f" {model_count}: {model_type}/{lang}/{dataset}/{model} [already downloaded]")
- else:
- print(f" {model_count}: {model_type}/{lang}/{dataset}/{model}")
+ if self.verbose:
+ if os.path.exists(output_path):
+ print(f" {model_count}: {model_type}/{lang}/{dataset}/{model} [already downloaded]")
+ else:
+ print(f" {model_count}: {model_type}/{lang}/{dataset}/{model}")
  model_list.append(f"{model_type}/{lang}/{dataset}/{model}")
  model_count += 1
  return model_list
 
  def _list_for_model_type(self, model_type):
- print(" Name format: language/dataset/model")
  models_name_list = []
  model_count = 1
  model_type = "tts_models"
  models_name_list.extend(self._list_models(model_type, model_count))
- return [name.replace(model_type + "/", "") for name in models_name_list]
+ return models_name_list
 
  def list_models(self):
- print(" Name format: type/language/dataset/model")
  models_name_list = []
  model_count = 1
  for model_type in self.models_dict:

TTS/utils/synthesizer.py

@@ -62,7 +62,6 @@ def __init__(
  self.tts_model = None
  self.vocoder_model = None
  self.speaker_manager = None
- self.num_speakers = 0
  self.tts_speakers = {}
  self.language_manager = None
  self.num_languages = 0

docs/source/inference.md

@@ -11,6 +11,7 @@ After the installation, 2 terminal commands are available.
 
 1. TTS Command Line Interface (CLI). - `tts`
 2. Local Demo Server. - `tts-server`
+3. In 🐍Python. - `from TTS.api import TTS`
 
 ## On the Commandline - `tts`
 ![cli.gif](https://github.com/coqui-ai/TTS/raw/main/images/tts_cli.gif)
@@ -99,5 +100,30 @@ tts-server --model_name "<type>/<language>/<dataset>/<model_name>" \
  --vocoder_name "<type>/<language>/<dataset>/<model_name>"
 ```
 
-## TorchHub
-You can also use [this simple colab notebook](https://colab.research.google.com/drive/1iAe7ZdxjUIuN6V4ooaCt0fACEGKEn7HW?usp=sharing) using TorchHub to synthesize speech.
+## Python API
+
+You can run a multi-speaker and multi-lingual model in Python as
+
+```python
+from TTS.api import TTS
+
+# List available 🐸TTS models and choose the first one
+model_name = TTS.list_models()[0]
+# Init TTS
+tts = TTS(model_name)
+# Run TTS
+# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language
+# Text to speech with a numpy output
+wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
+# Text to speech to a file
+tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")
+```
+
+Here is an example for a single speaker model.
+
+```python
+# Init TTS with the target model name
+tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
+# Run TTS
+tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH)
+```

tests/inference_tests/test_python_api.py

@@ -0,0 +1,36 @@
+import os
+import unittest
+
+from tests import get_tests_output_path
+from TTS.api import TTS
+
+OUTPUT_PATH = os.path.join(get_tests_output_path(), "test_python_api.wav")
+
+
+class TTSTest(unittest.TestCase):
+ def test_single_speaker_model(self):
+ tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
+
+ error_raised = False
+ try:
+ tts.tts_to_file(text="Ich bin eine Testnachricht.", speaker="Thorsten", language="de")
+ except ValueError:
+ error_raised = True
+
+ tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH)
+
+ self.assertTrue(error_raised)
+ self.assertFalse(tts.is_multi_speaker)
+ self.assertFalse(tts.is_multi_lingual)
+ self.assertIsNone(tts.speakers)
+ self.assertIsNone(tts.languages)
+
+ def test_multi_speaker_multi_lingual_model(self):
+ tts = TTS()
+ tts.load_model_by_name(tts.models[0]) # YourTTS
+ tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path=OUTPUT_PATH)
+
+ self.assertTrue(tts.is_multi_speaker)
+ self.assertTrue(tts.is_multi_lingual)
+ self.assertGreater(len(tts.speakers), 1)
+ self.assertGreater(len(tts.languages), 1)