Medix-AI/coqui-tts
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coqui-tts
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Merge pull request #2499 from coqui-ai/dev 

✨ v0.13.1
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Eren Gölge 2023-04-12 16:58:33 +02:00 committed by GitHub
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14
README.md
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@ -1,10 +1,13 @@
<img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
----
### 📣 Clone your voice with a single click on [🐸Coqui.ai](https://app.coqui.ai/auth/signin)
## 🐸Coqui.ai News
- 📣 Coqui Studio API is landed on 🐸TTS. You can use the studio voices in combination with 🐸TTS models. [Example](https://github.com/coqui-ai/TTS/edit/dev/README.md#-python-api)
- 📣 Voice generation with prompts - **Prompt to Voice** - is live on Coqui.ai!! [Blog Post](https://coqui.ai/blog/tts/prompt-to-voice)
- 📣 Clone your voice with a single click on [🐸Coqui.ai](https://app.coqui.ai/auth/signin)
<br>
## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
----
🐸TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality.
🐸TTS comes with pretrained models, tools for measuring dataset quality and already used in **20+ languages** for products and research projects.
@ -123,6 +126,9 @@ Underlined "TTS*" and "Judy*" are 🐸TTS models
- HiFiGAN: [paper](https://arxiv.org/abs/2010.05646)
- UnivNet: [paper](https://arxiv.org/abs/2106.07889)
### Voice Conversion
- FreeVC: [paper](https://arxiv.org/abs/2210.15418)
You can also help us implement more models.
## Install TTS

2
TTS/VERSION
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@ -1 +1 @@
0.13.0
0.13.1

43
TTS/api.py
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@ -7,6 +7,7 @@ from pathlib import Path
from typing import Tuple
import numpy as np
import requests
from scipy.io import wavfile
from TTS.utils.audio.numpy_transforms import save_wav
@ -65,6 +66,11 @@ class CS_API:
self._speakers = None
self._check_token()
@staticmethod
def ping_api():
URL = "https://coqui.gateway.scarf.sh/tts/api"
_ = requests.get(URL)
@property
def speakers(self):
if self._speakers is None:
@ -80,12 +86,13 @@ class CS_API:
return ["Neutral", "Happy", "Sad", "Angry", "Dull"]
def _check_token(self):
self.ping_api()
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"
"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"
""
@ -273,8 +280,11 @@ class TTS:
self.csapi = None
self.model_name = None
if model_name:
self.load_tts_model_by_name(model_name, gpu)
if model_name is not None:
if "tts_models" in model_name or "coqui_studio" in model_name:
self.load_tts_model_by_name(model_name, gpu)
elif "voice_conversion_models" in model_name:
self.load_vc_model_by_name(model_name, gpu)
if model_path:
self.load_tts_model_by_path(
@ -342,6 +352,7 @@ class TTS:
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.
"""
self.model_name = model_name
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)
@ -565,19 +576,39 @@ class TTS:
def voice_conversion(
self,
sourve_wav: str,
source_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.voice_converter.voice_conversion(source_wav=source_wav, target_wav=target_wav)
return wav
def voice_conversion_to_file(
self,
source_wav: str,
target_wav: str,
file_path: str = "output.wav",
):
"""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.
file_path (str, optional):
Output file path. Defaults to "output.wav".
"""
wav = self.synthesizer.voice_conversion(source_wav=sourve_wav, target_wav=target_wav)
return wav
wav = self.voice_conversion(source_wav=source_wav, target_wav=target_wav)
save_wav(wav=wav, path=file_path, sample_rate=self.voice_converter.vc_config.audio.output_sample_rate)
return file_path
def tts_with_vc(self, text: str, language: str = None, speaker_wav: str = None):
"""Convert text to speech with voice conversion.

4
TTS/tts/models/vits.py
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@ -149,7 +149,7 @@ def spec_to_mel(spec, n_fft, num_mels, sample_rate, fmin, fmax):
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sample_rate, n_fft, num_mels, fmin, fmax)
mel = librosa_mel_fn(sr=sample_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
mel = torch.matmul(mel_basis[fmax_dtype_device], spec)
mel = amp_to_db(mel)
@ -176,7 +176,7 @@ def wav_to_mel(y, n_fft, num_mels, sample_rate, hop_length, win_length, fmin, fm
fmax_dtype_device = str(fmax) + "_" + dtype_device
wnsize_dtype_device = str(win_length) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sample_rate, n_fft, num_mels, fmin, fmax)
mel = librosa_mel_fn(sr=sample_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_length).to(dtype=y.dtype, device=y.device)

4
TTS/utils/audio/numpy_transforms.py
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@ -269,7 +269,7 @@ def compute_f0(
np.ndarray: Pitch. Shape :math:`[T_pitch,]`. :math:`T_pitch == T_wav / hop_length`
Examples:
>>> WAV_FILE = filename = librosa.util.example_audio_file()
>>> WAV_FILE = filename = librosa.example('vibeace')
>>> from TTS.config import BaseAudioConfig
>>> from TTS.utils.audio import AudioProcessor
>>> conf = BaseAudioConfig(pitch_fmax=640, pitch_fmin=1)
@ -310,7 +310,7 @@ def compute_energy(y: np.ndarray, **kwargs) -> np.ndarray:
Returns:
np.ndarray: energy. Shape :math:`[T_energy,]`. :math:`T_energy == T_wav / hop_length`
Examples:
>>> WAV_FILE = filename = librosa.util.example_audio_file()
>>> WAV_FILE = filename = librosa.example('vibeace')
>>> from TTS.config import BaseAudioConfig
>>> from TTS.utils.audio import AudioProcessor
>>> conf = BaseAudioConfig()

6
TTS/utils/audio/processor.py
View File

@ -243,7 +243,7 @@ class AudioProcessor(object):
if self.mel_fmax is not None:
assert self.mel_fmax <= self.sample_rate // 2
return librosa.filters.mel(
self.sample_rate, self.fft_size, n_mels=self.num_mels, fmin=self.mel_fmin, fmax=self.mel_fmax
sr=self.sample_rate, n_fft=self.fft_size, n_mels=self.num_mels, fmin=self.mel_fmin, fmax=self.mel_fmax
)
def _stft_parameters(
@ -569,7 +569,7 @@ class AudioProcessor(object):
np.ndarray: Pitch.
Examples:
>>> WAV_FILE = filename = librosa.util.example_audio_file()
>>> WAV_FILE = filename = librosa.example('vibeace')
>>> from TTS.config import BaseAudioConfig
>>> from TTS.utils.audio import AudioProcessor
>>> conf = BaseAudioConfig(pitch_fmax=640, pitch_fmin=1)
@ -711,7 +711,7 @@ class AudioProcessor(object):
Args:
filename (str): Path to the wav file.
"""
return librosa.get_duration(filename)
return librosa.get_duration(filename=filename)
@staticmethod
def mulaw_encode(wav: np.ndarray, qc: int) -> np.ndarray:

4
TTS/utils/audio/torch_transforms.py
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@ -144,8 +144,8 @@ class TorchSTFT(nn.Module): # pylint: disable=abstract-method
def _build_mel_basis(self):
mel_basis = librosa.filters.mel(
self.sample_rate,
self.n_fft,
sr=self.sample_rate,
n_fft=self.n_fft,
n_mels=self.n_mels,
fmin=self.mel_fmin,
fmax=self.mel_fmax,

2
requirements.txt
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@ -6,7 +6,7 @@ scipy>=1.4.0
torch>=1.7
torchaudio
soundfile
librosa==0.8.0
librosa==0.10.0.*
numba==0.55.1;python_version<"3.9"
numba==0.56.4;python_version>="3.9"
inflect==5.6.0

8
tests/inference_tests/test_python_api.py
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@ -93,3 +93,11 @@ class TTSTest(unittest.TestCase):
tts = TTS()
tts.load_tts_model_by_name("tts_models/multilingual/multi-dataset/your_tts")
tts.tts_to_file("Hello world!", speaker_wav=cloning_test_wav_path, language="en", file_path=OUTPUT_PATH)
def test_voice_conversion(self): # pylint: disable=no-self-use
tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=False)
tts.voice_conversion_to_file(
source_wav=cloning_test_wav_path,
target_wav=cloning_test_wav_path,
file_path=OUTPUT_PATH,
)