Inference API for 🐶Bark (coqui-ai#2685)

* Add bark requirements

* Draft Bark implementation

* Download HF models

* Update synthesizer

* Add bark model

* Make style

* Update pylintrc

* Update model URLs

* Update Bark Config

* Fix here and ther

* Make style

* Make lint

* Update requirements

* Update requirements

Author: erogol

.pylintrc

@@ -169,7 +169,9 @@ disable=missing-docstring,
         comprehension-escape,
         duplicate-code,
         not-callable,
-        import-outside-toplevel
+        import-outside-toplevel,
+        logging-fstring-interpolation,
+        logging-not-lazy
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

TTS/.models.json

@@ -346,7 +346,7 @@ def list_models():
 
     def download_model_by_name(self, model_name: str):
         model_path, config_path, model_item = self.manager.download_model(model_name)
-        if "fairseq" in model_name or (model_item is not None and isinstance(model_item["github_rls_url"], list)):
+        if "fairseq" in model_name or (model_item is not None and isinstance(model_item["model_url"], list)):
             # return model directory if there are multiple files
             # we assume that the model knows how to load itself
             return None, None, None, None, model_path
@@ -584,6 +584,8 @@ def tts_to_file(
                 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".
+            kwargs (dict, optional):
+                Additional arguments for the model.
         """
         self._check_arguments(speaker=speaker, language=language, speaker_wav=speaker_wav, **kwargs)
 

TTS/api.py

@@ -356,7 +356,7 @@ def main():
             vc_config_path = config_path
 
         # tts model with multiple files to be loaded from the directory path
-        if model_item.get("author", None) == "fairseq" or isinstance(model_item["github_rls_url"], list):
+        if model_item.get("author", None) == "fairseq" or isinstance(model_item["model_url"], list):
             model_dir = model_path
             tts_path = None
             tts_config_path = None

TTS/bin/synthesize.py

@@ -0,0 +1,105 @@
+import os
+from dataclasses import dataclass
+from typing import Dict
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.layers.bark.model import GPTConfig
+from TTS.tts.layers.bark.model_fine import FineGPTConfig
+from TTS.tts.models.bark import BarkAudioConfig
+from TTS.utils.generic_utils import get_user_data_dir
+
+
+@dataclass
+class BarkConfig(BaseTTSConfig):
+    """Bark TTS configuration
+
+    Args:
+        model (str): model name that registers the model.
+        audio (BarkAudioConfig): audio configuration. Defaults to BarkAudioConfig().
+        num_chars (int): number of characters in the alphabet. Defaults to 0.
+        semantic_config (GPTConfig): semantic configuration. Defaults to GPTConfig().
+        fine_config (FineGPTConfig): fine configuration. Defaults to FineGPTConfig().
+        coarse_config (GPTConfig): coarse configuration. Defaults to GPTConfig().
+        CONTEXT_WINDOW_SIZE (int): GPT context window size. Defaults to 1024.
+        SEMANTIC_RATE_HZ (float): semantic tokens rate in Hz. Defaults to 49.9.
+        SEMANTIC_VOCAB_SIZE (int): semantic vocabulary size. Defaults to 10_000.
+        CODEBOOK_SIZE (int): encodec codebook size. Defaults to 1024.
+        N_COARSE_CODEBOOKS (int): number of coarse codebooks. Defaults to 2.
+        N_FINE_CODEBOOKS (int): number of fine codebooks. Defaults to 8.
+        COARSE_RATE_HZ (int): coarse tokens rate in Hz. Defaults to 75.
+        SAMPLE_RATE (int): sample rate. Defaults to 24_000.
+        USE_SMALLER_MODELS (bool): use smaller models. Defaults to False.
+        TEXT_ENCODING_OFFSET (int): text encoding offset. Defaults to 10_048.
+        SEMANTIC_PAD_TOKEN (int): semantic pad token. Defaults to 10_000.
+        TEXT_PAD_TOKEN ([type]): text pad token. Defaults to 10_048.
+        TEXT_EOS_TOKEN ([type]): text end of sentence token. Defaults to 10_049.
+        TEXT_SOS_TOKEN ([type]): text start of sentence token. Defaults to 10_050.
+        SEMANTIC_INFER_TOKEN (int): semantic infer token. Defaults to 10_051.
+        COARSE_SEMANTIC_PAD_TOKEN (int): coarse semantic pad token. Defaults to 12_048.
+        COARSE_INFER_TOKEN (int): coarse infer token. Defaults to 12_050.
+        REMOTE_BASE_URL ([type]): remote base url. Defaults to "https://huggingface.co/erogol/bark/tree".
+        REMOTE_MODEL_PATHS (Dict): remote model paths. Defaults to None.
+        LOCAL_MODEL_PATHS (Dict): local model paths. Defaults to None.
+        SMALL_REMOTE_MODEL_PATHS (Dict): small remote model paths. Defaults to None.
+        CACHE_DIR (str): local cache directory. Defaults to get_user_data_dir().
+        DEF_SPEAKER_DIR (str): default speaker directory to stoke speaker values for voice cloning. Defaults to get_user_data_dir().
+    """
+
+    model: str = "bark"
+    audio: BarkAudioConfig = BarkAudioConfig()
+    num_chars: int = 0
+    semantic_config: GPTConfig = GPTConfig()
+    fine_config: FineGPTConfig = FineGPTConfig()
+    coarse_config: GPTConfig = GPTConfig()
+    CONTEXT_WINDOW_SIZE: int = 1024
+    SEMANTIC_RATE_HZ: float = 49.9
+    SEMANTIC_VOCAB_SIZE: int = 10_000
+    CODEBOOK_SIZE: int = 1024
+    N_COARSE_CODEBOOKS: int = 2
+    N_FINE_CODEBOOKS: int = 8
+    COARSE_RATE_HZ: int = 75
+    SAMPLE_RATE: int = 24_000
+    USE_SMALLER_MODELS: bool = False
+
+    TEXT_ENCODING_OFFSET: int = 10_048
+    SEMANTIC_PAD_TOKEN: int = 10_000
+    TEXT_PAD_TOKEN: int = 129_595
+    SEMANTIC_INFER_TOKEN: int = 129_599
+    COARSE_SEMANTIC_PAD_TOKEN: int = 12_048
+    COARSE_INFER_TOKEN: int = 12_050
+
+    REMOTE_BASE_URL = "https://huggingface.co/erogol/bark/tree/main/"
+    REMOTE_MODEL_PATHS: Dict = None
+    LOCAL_MODEL_PATHS: Dict = None
+    SMALL_REMOTE_MODEL_PATHS: Dict = None
+    CACHE_DIR: str = str(get_user_data_dir("tts/suno/bark_v0"))
+    DEF_SPEAKER_DIR: str = str(get_user_data_dir("tts/bark_v0/speakers"))
+
+    def __post_init__(self):
+        self.REMOTE_MODEL_PATHS = {
+            "text": {
+                "path": os.path.join(self.REMOTE_BASE_URL, "text_2.pt"),
+                "checksum": "54afa89d65e318d4f5f80e8e8799026a",
+            },
+            "coarse": {
+                "path": os.path.join(self.REMOTE_BASE_URL, "coarse_2.pt"),
+                "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
+            },
+            "fine": {
+                "path": os.path.join(self.REMOTE_BASE_URL, "fine_2.pt"),
+                "checksum": "59d184ed44e3650774a2f0503a48a97b",
+            },
+        }
+        self.LOCAL_MODEL_PATHS = {
+            "text": os.path.join(self.CACHE_DIR, "text_2.pt"),
+            "coarse": os.path.join(self.CACHE_DIR, "coarse_2.pt"),
+            "fine": os.path.join(self.CACHE_DIR, "fine_2.pt"),
+            "hubert_tokenizer": os.path.join(self.CACHE_DIR, "tokenizer.pth"),
+            "hubert": os.path.join(self.CACHE_DIR, "hubert.pt"),
+        }
+        self.SMALL_REMOTE_MODEL_PATHS = {
+            "text": {"path": os.path.join(self.REMOTE_BASE_URL, "text.pt")},
+            "coarse": {"path": os.path.join(self.REMOTE_BASE_URL, "coarse.pt")},
+            "fine": {"path": os.path.join(self.REMOTE_BASE_URL, "fine.pt")},
+        }
+        self.sample_rate = self.SAMPLE_RATE  # pylint: disable=attribute-defined-outside-init

TTS/tts/configs/bark_config.py

@@ -0,0 +1,35 @@
+# From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer
+
+import os.path
+import shutil
+import urllib.request
+
+import huggingface_hub
+
+
+class HubertManager:
+    @staticmethod
+    def make_sure_hubert_installed(
+        download_url: str = "https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt", model_path: str = ""
+    ):
+        if not os.path.isfile(model_path):
+            print("Downloading HuBERT base model")
+            urllib.request.urlretrieve(download_url, model_path)
+            print("Downloaded HuBERT")
+            return model_path
+        return None
+
+    @staticmethod
+    def make_sure_tokenizer_installed(
+        model: str = "quantifier_hubert_base_ls960_14.pth",
+        repo: str = "GitMylo/bark-voice-cloning",
+        model_path: str = "",
+    ):
+        model_dir = os.path.dirname(model_path)
+        if not os.path.isfile(model_path):
+            print("Downloading HuBERT custom tokenizer")
+            huggingface_hub.hf_hub_download(repo, model, local_dir=model_dir, local_dir_use_symlinks=False)
+            shutil.move(os.path.join(model_dir, model), model_path)
+            print("Downloaded tokenizer")
+            return model_path
+        return None

TTS/tts/layers/bark/__init__.py

@@ -0,0 +1,101 @@
+"""
+Modified HuBERT model without kmeans.
+Original author: https://github.com/lucidrains/
+Modified by: https://www.github.com/gitmylo/
+License: MIT
+"""
+
+# Modified code from https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch/hubert_kmeans.py
+
+import logging
+from pathlib import Path
+
+import fairseq
+import torch
+from einops import pack, unpack
+from torch import nn
+from torchaudio.functional import resample
+
+logging.root.setLevel(logging.ERROR)
+
+
+def round_down_nearest_multiple(num, divisor):
+    return num // divisor * divisor
+
+
+def curtail_to_multiple(t, mult, from_left=False):
+    data_len = t.shape[-1]
+    rounded_seq_len = round_down_nearest_multiple(data_len, mult)
+    seq_slice = slice(None, rounded_seq_len) if not from_left else slice(-rounded_seq_len, None)
+    return t[..., seq_slice]
+
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    return val if exists(val) else d
+
+
+class CustomHubert(nn.Module):
+    """
+    checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert
+    or you can train your own
+    """
+
+    def __init__(self, checkpoint_path, target_sample_hz=16000, seq_len_multiple_of=None, output_layer=9, device=None):
+        super().__init__()
+        self.target_sample_hz = target_sample_hz
+        self.seq_len_multiple_of = seq_len_multiple_of
+        self.output_layer = output_layer
+
+        if device is not None:
+            self.to(device)
+
+        model_path = Path(checkpoint_path)
+
+        assert model_path.exists(), f"path {checkpoint_path} does not exist"
+
+        checkpoint = torch.load(checkpoint_path)
+        load_model_input = {checkpoint_path: checkpoint}
+        model, *_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(load_model_input)
+
+        if device is not None:
+            model[0].to(device)
+
+        self.model = model[0]
+        self.model.eval()
+
+    @property
+    def groups(self):
+        return 1
+
+    @torch.no_grad()
+    def forward(self, wav_input, flatten=True, input_sample_hz=None):
+        device = wav_input.device
+
+        if exists(input_sample_hz):
+            wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz)
+
+        if exists(self.seq_len_multiple_of):
+            wav_input = curtail_to_multiple(wav_input, self.seq_len_multiple_of)
+
+        embed = self.model(
+            wav_input,
+            features_only=True,
+            mask=False,  # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code
+            output_layer=self.output_layer,
+        )
+
+        embed, packed_shape = pack([embed["x"]], "* d")
+
+        # codebook_indices = self.kmeans.predict(embed.cpu().detach().numpy())
+
+        codebook_indices = torch.from_numpy(embed.cpu().detach().numpy()).to(device)  # .long()
+
+        if flatten:
+            return codebook_indices
+
+        (codebook_indices,) = unpack(codebook_indices, packed_shape, "*")
+        return codebook_indices