TTS/api.py

import http.client
import json
import os
import tempfile
import urllib.request
from pathlib import Path
from typing import Tuple

import numpy as np
from scipy.io import wavfile

from TTS.utils.audio.numpy_transforms import save_wav
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer


class Speaker(object):
    """Convert dict to object."""

    def __init__(self, d, is_voice=False):
        self.is_voice = is_voice
        for k, v in d.items():
            if isinstance(k, (list, tuple)):
                setattr(self, k, [Speaker(x) if isinstance(x, dict) else x for x in v])
            else:
                setattr(self, k, Speaker(v) if isinstance(v, dict) else v)

    def __repr__(self):
        return str(self.__dict__)


class CS_API:
    """🐸Coqui Studio API Wrapper.

    🐸Coqui Studio is the most advanced voice generation platform. You can generate new voices by voice cloning, voice
    interpolation, or our unique prompt to voice technology. It also provides a set of built-in voices with different
    characteristics. You can use these voices to generate new audio files or use them in your applications.
    You can use all the built-in and your own 🐸Coqui Studio speakers with this API with an API token.
    You can signup to 🐸Coqui Studio from https://app.coqui.ai/auth/signup and get an API token from
    https://app.coqui.ai/account. We can either enter the token as an environment variable as
    `export COQUI_STUDIO_TOKEN=<token>` or pass it as `CS_API(api_token=<toke>)`.
    Visit https://app.coqui.ai/api for more information.

    Example listing all available speakers:
        >>> from TTS.api import CS_API
        >>> tts = CS_API()
        >>> tts.speakers

    Example listing all emotions:
        >>> from TTS.api import CS_API
        >>> tts = CS_API()
        >>> tts.emotions

    Example with a built-in 🐸 speaker:
        >>> from TTS.api import CS_API
        >>> tts = CS_API()
        >>> wav, sr = api.tts("Hello world", speaker_name="Claribel Dervla")
        >>> filepath = tts.tts_to_file(text="Hello world!", speaker_name=tts.speakers[0].name, file_path="output.wav")
    """

    def __init__(self, api_token=None):
        self.api_token = api_token
        self.api_prefix = "/api/v2"
        self.headers = None
        self._speakers = None
        self._check_token()

    @property
    def speakers(self):
        if self._speakers is None:
            self._speakers = self.list_all_speakers()
        return self._speakers

    @property
    def emotions(self):
        """Return a list of available emotions.

        TODO: Get this from the API endpoint.
        """
        return ["Neutral", "Happy", "Sad", "Angry", "Dull"]

    def _check_token(self):
        if self.api_token is None:
            self.api_token = os.environ.get("COQUI_STUDIO_TOKEN")
            self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_token}"}
        if not self.api_token:
            raise ValueError(
                "No API token found for 🐸Coqui Studio voices - https://coqui.ai.\n"
                "Visit 🔗https://app.coqui.ai/account to get one.\n"
                "Set it as an environment variable `export COQUI_STUDIO_TOKEN=<token>`\n"
                ""
            )

    def list_all_speakers(self):
        """Return both built-in Coqui Studio speakers and custom voices created by the user."""
        return self.list_speakers() + self.list_voices()

    def list_speakers(self):
        """List built-in Coqui Studio speakers."""
        self._check_token()
        conn = http.client.HTTPSConnection("app.coqui.ai")
        conn.request("GET", f"{self.api_prefix}/speakers", headers=self.headers)
        res = conn.getresponse()
        data = res.read()
        return [Speaker(s) for s in json.loads(data)["result"]]

    def list_voices(self):
        """List custom voices created by the user."""
        conn = http.client.HTTPSConnection("app.coqui.ai")
        conn.request("GET", f"{self.api_prefix}/voices", headers=self.headers)
        res = conn.getresponse()
        data = res.read()
        return [Speaker(s, True) for s in json.loads(data)["result"]]

    def list_speakers_as_tts_models(self):
        """List speakers in ModelManager format."""
        models = []
        for speaker in self.speakers:
            model = f"coqui_studio/en/{speaker.name}/coqui_studio"
            models.append(model)
        return models

    def name_to_speaker(self, name):
        for speaker in self.speakers:
            if speaker.name == name:
                return speaker
        raise ValueError(f"Speaker {name} not found.")

    def id_to_speaker(self, speaker_id):
        for speaker in self.speakers:
            if speaker.id == speaker_id:
                return speaker
        raise ValueError(f"Speaker {speaker_id} not found.")

    @staticmethod
    def url_to_np(url):
        tmp_file, _ = urllib.request.urlretrieve(url)
        rate, data = wavfile.read(tmp_file)
        return data, rate

    @staticmethod
    def _create_payload(text, speaker, emotion, speed):
        payload = {}
        if speaker.is_voice:
            payload["voice_id"] = speaker.id
        else:
            payload["speaker_id"] = speaker.id
        payload.update(
            {
                "emotion": emotion,
                "name": speaker.name,
                "text": text,
                "speed": speed,
            }
        )
        return payload

    def tts(
        self,
        text: str,
        speaker_name: str = None,
        speaker_id=None,
        emotion="Neutral",
        speed=1.0,
        language=None,  # pylint: disable=unused-argument
    ) -> Tuple[np.ndarray, int]:
        """Synthesize speech from text.

        Args:
            text (str): Text to synthesize.
            speaker_name (str): Name of the speaker. You can get the list of speakers with `list_speakers()` and
                voices (user generated speakers) with `list_voices()`.
            speaker_id (str): Speaker ID. If None, the speaker name is used.
            emotion (str): Emotion of the speaker. One of "Neutral", "Happy", "Sad", "Angry", "Dull".
            speed (float): Speed of the speech. 1.0 is normal speed.
            language (str): Language of the text. If None, the default language of the speaker is used.
        """
        self._check_token()
        if speaker_name is None and speaker_id is None:
            raise ValueError(" [!] Please provide either a `speaker_name` or a `speaker_id`.")
        if speaker_id is None:
            speaker = self.name_to_speaker(speaker_name)
        else:
            speaker = self.id_to_speaker(speaker_id)
        conn = http.client.HTTPSConnection("app.coqui.ai")
        payload = self._create_payload(text, speaker, emotion, speed)
        conn.request("POST", "/api/v2/samples", json.dumps(payload), self.headers)
        res = conn.getresponse()
        data = res.read()
        try:
            wav, sr = self.url_to_np(json.loads(data)["audio_url"])
        except KeyError as e:
            raise ValueError(f" [!] 🐸 API returned error: {data}") from e
        return wav, sr

    def tts_to_file(
        self,
        text: str,
        speaker_name: str,
        speaker_id=None,
        emotion="Neutral",
        speed=1.0,
        language=None,
        file_path: str = None,
    ) -> str:
        """Synthesize speech from text and save it to a file.

        Args:
            text (str): Text to synthesize.
            speaker_name (str): Name of the speaker. You can get the list of speakers with `list_speakers()` and
                voices (user generated speakers) with `list_voices()`.
            speaker_id (str): Speaker ID. If None, the speaker name is used.
            emotion (str): Emotion of the speaker. One of "Neutral", "Happy", "Sad", "Angry", "Dull".
            speed (float): Speed of the speech. 1.0 is normal speed.
            language (str): Language of the text. If None, the default language of the speaker is used.
            file_path (str): Path to save the file. If None, a temporary file is created.
        """
        if file_path is None:
            file_path = tempfile.mktemp(".wav")
        wav, sr = self.tts(text, speaker_name, speaker_id, emotion, speed, language)
        wavfile.write(file_path, sr, wav)
        return file_path


class TTS:
    """TODO: Add voice conversion and Capacitron support."""

    def __init__(
        self,
        model_name: str = None,
        model_path: str = None,
        config_path: str = None,
        vocoder_path: str = None,
        vocoder_config_path: 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")

        Example loading a model from a path:
            >>> tts = TTS(model_path="/path/to/checkpoint_100000.pth", config_path="/path/to/config.json", progress_bar=False, gpu=False)
            >>> tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path="output.wav")

        Example voice cloning with YourTTS in English, French and Portuguese:
            >>> tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True)
            >>> tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="thisisit.wav")
            >>> tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr", file_path="thisisit.wav")
            >>> tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt", file_path="thisisit.wav")

        Args:
            model_name (str, optional): Model name to load. You can list models by ```tts.models```. Defaults to None.
            model_path (str, optional): Path to the model checkpoint. Defaults to None.
            config_path (str, optional): Path to the model config. Defaults to None.
            vocoder_path (str, optional): Path to the vocoder checkpoint. Defaults to None.
            vocoder_config_path (str, optional): Path to the vocoder config. 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
        self.voice_converter = None
        self.csapi = None
        self.model_name = None

        if model_name:
            self.load_tts_model_by_name(model_name, gpu)

        if model_path:
            self.load_tts_model_by_path(
                model_path, config_path, vocoder_path=vocoder_path, vocoder_config=vocoder_config_path, gpu=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_coqui_studio(self):
        return "coqui_studio" in self.model_name

    @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():
        try:
            csapi = CS_API()
            models = csapi.list_speakers_as_tts_models()
        except ValueError as e:
            print(e)
            models = []
        manager = ModelManager(models_file=TTS.get_models_file_path(), progress_bar=False, verbose=False)
        return manager.list_tts_models() + 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.get("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_vc_model_by_name(self, model_name: str, gpu: bool = False):
        """Load one of the voice conversion models by name.

        Args:
            model_name (str): Model name to load. You can list models by ```tts.models```.
            gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
        """
        model_path, config_path, _, _ = self.download_model_by_name(model_name)
        self.voice_converter = Synthesizer(vc_checkpoint=model_path, vc_config=config_path, use_cuda=gpu)

    def load_tts_model_by_name(self, model_name: str, gpu: bool = False):
        """Load one of 🐸TTS models by name.

        Args:
            model_name (str): Model name to load. You can list models by ```tts.models```.
            gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.

        TODO: Add tests
        """
        self.synthesizer = None
        self.csapi = None
        self.model_name = model_name

        if "coqui_studio" in model_name:
            self.csapi = CS_API()
        else:
            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 load_tts_model_by_path(
        self, model_path: str, config_path: str, vocoder_path: str = None, vocoder_config: str = None, gpu: bool = False
    ):
        """Load a model from a path.

        Args:
            model_path (str): Path to the model checkpoint.
            config_path (str): Path to the model config.
            vocoder_path (str, optional): Path to the vocoder checkpoint. Defaults to None.
            vocoder_config (str, optional): Path to the vocoder config. Defaults to None.
            gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
        """

        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,
            encoder_checkpoint=None,
            encoder_config=None,
            use_cuda=gpu,
        )

    def _check_arguments(
        self,
        speaker: str = None,
        language: str = None,
        speaker_wav: str = None,
        emotion: str = None,
        speed: float = None,
    ) -> None:
        """Check if the arguments are valid for the model."""
        if not self.is_coqui_studio:
            # check for the coqui tts models
            if self.is_multi_speaker and (speaker is None and speaker_wav 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.")
            if not emotion is None and not speed is None:
                raise ValueError("Emotion and speed can only be used with Coqui Studio models.")
        else:
            if emotion is None:
                emotion = "Neutral"
            if speed is None:
                speed = 1.0
            # check for the studio models
            if speaker_wav is not None:
                raise ValueError("Coqui Studio models do not support `speaker_wav` argument.")
            if speaker is not None:
                raise ValueError("Coqui Studio models do not support `speaker` argument.")
            if language is not None and language != "en":
                raise ValueError("Coqui Studio models currently support only `language=en` argument.")
            if emotion not in ["Neutral", "Happy", "Sad", "Angry", "Dull"]:
                raise ValueError(f"Emotion - `{emotion}` - must be one of `Neutral`, `Happy`, `Sad`, `Angry`, `Dull`.")

    def tts_coqui_studio(
        self,
        text: str,
        speaker_name: str = None,
        language: str = None,
        emotion: str = "Neutral",
        speed: float = 1.0,
        file_path: str = None,
    ):
        """Convert text to speech using Coqui Studio models. Use `CS_API` class if you are only interested in the API.

        Args:
            text (str):
                Input text to synthesize.
            speaker_name (str, optional):
                Speaker name from Coqui Studio. Defaults to None.
            language (str, optional):
                Language code. Coqui Studio currently supports only English. Defaults to None.
            emotion (str, optional):
                Emotion of the speaker. One of "Neutral", "Happy", "Sad", "Angry", "Dull". Defaults to "Neutral".
            speed (float, optional):
                Speed of the speech. Defaults to 1.0.
            file_path (str, optional):
                Path to save the output file. When None it returns the `np.ndarray` of waveform. Defaults to None.
        """
        speaker_name = self.model_name.split("/")[2]
        if file_path is None:
            return self.csapi.tts_to_file(
                text=text,
                speaker_name=speaker_name,
                language=language,
                speed=speed,
                emotion=emotion,
                file_path=file_path,
            )[0]
        return self.csapi.tts(text=text, speaker_name=speaker_name, language=language, speed=speed, emotion=emotion)[0]

    def tts(
        self,
        text: str,
        speaker: str = None,
        language: str = None,
        speaker_wav: str = None,
        emotion: str = None,
        speed: float = 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.
            speaker_wav (str, optional):
                Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
                Defaults to None.
            emotion (str, optional):
                Emotion to use for 🐸Coqui Studio models. If None, Studio models use "Neutral". Defaults to None.
            speed (float, optional):
                Speed factor to use for 🐸Coqui Studio models, between 0 and 2.0. If None, Studio models use 1.0.
                Defaults to None.
        """
        self._check_arguments(speaker=speaker, language=language, speaker_wav=speaker_wav, emotion=emotion, speed=speed)
        if self.csapi is not None:
            return self.tts_coqui_studio(
                text=text, speaker_name=speaker, language=language, emotion=emotion, speed=speed
            )

        wav = self.synthesizer.tts(
            text=text,
            speaker_name=speaker,
            language_name=language,
            speaker_wav=speaker_wav,
            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,
        speaker_wav: str = None,
        emotion: str = "Neutral",
        speed: float = 1.0,
        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.
            speaker_wav (str, optional):
                Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
                Defaults to None.
            emotion (str, optional):
                Emotion to use for 🐸Coqui Studio models. Defaults to "Neutral".
            speed (float, optional):
                Speed factor to use for 🐸Coqui Studio models, between 0.0 and 2.0. Defaults to None.
            file_path (str, optional):
                Output file path. Defaults to "output.wav".
        """
        self._check_arguments(speaker=speaker, language=language, speaker_wav=speaker_wav)

        if self.csapi is not None:
            return self.tts_coqui_studio(
                text=text, speaker_name=speaker, language=language, emotion=emotion, speed=speed, file_path=file_path
            )
        wav = self.tts(text=text, speaker=speaker, language=language, speaker_wav=speaker_wav)
        self.synthesizer.save_wav(wav=wav, path=file_path)
        return file_path

    def voice_conversion(
        self,
        sourve_wav: str,
        target_wav: str,
    ):
        """Voice conversion with FreeVC. Convert source wav to target speaker.

        Args:
            source_wav (str):
                Path to the source wav file.
            target_wav (str):
                Path to the target wav file.
        """
        wav = self.synthesizer.voice_conversion(source_wav=sourve_wav, target_wav=target_wav)
        return wav

    def tts_with_vc(self, text: str, language: str = None, speaker_wav: str = None):
        """Convert text to speech with voice conversion.

        It combines tts with voice conversion to fake voice cloning.

        - Convert text to speech with tts.
        - Convert the output wav to target speaker with voice conversion.

        Args:
            text (str):
                Input text to synthesize.
            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.
            speaker_wav (str, optional):
                Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
                Defaults to None.
        """
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
            # Lazy code... save it to a temp file to resample it while reading it for VC
            self.tts_to_file(text=text, speaker=None, language=language, file_path=fp.name)
        if self.voice_converter is None:
            self.load_vc_model_by_name("voice_conversion_models/multilingual/vctk/freevc24")
        wav = self.voice_converter.voice_conversion(source_wav=fp.name, target_wav=speaker_wav)
        return wav

    def tts_with_vc_to_file(
        self, text: str, language: str = None, speaker_wav: str = None, file_path: str = "output.wav"
    ):
        """Convert text to speech with voice conversion and save to file.

        Check `tts_with_vc` for more details.

        Args:
            text (str):
                Input text to synthesize.
            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.
            speaker_wav (str, optional):
                Path to a reference wav file to use for voice cloning with supporting models like YourTTS.
                Defaults to None.
            file_path (str, optional):
                Output file path. Defaults to "output.wav".
        """
        wav = self.tts_with_vc(text=text, language=language, speaker_wav=speaker_wav)
        save_wav(wav=wav, path=file_path, sample_rate=self.voice_converter.vc_config.audio.output_sample_rate)