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# coding=utf-8 | |
# Copyright 2023 The Suno AI Authors and The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" | |
Processor class for Bark | |
""" | |
import json | |
import os | |
from typing import Optional | |
import numpy as np | |
from ...feature_extraction_utils import BatchFeature | |
from ...processing_utils import ProcessorMixin | |
from ...utils import logging | |
from ...utils.hub import get_file_from_repo | |
from ..auto import AutoTokenizer | |
logger = logging.get_logger(__name__) | |
class BarkProcessor(ProcessorMixin): | |
r""" | |
Constructs a Bark processor which wraps a text tokenizer and optional Bark voice presets into a single processor. | |
Args: | |
tokenizer ([`PreTrainedTokenizer`]): | |
An instance of [`PreTrainedTokenizer`]. | |
speaker_embeddings (`Dict[Dict[str]]`, *optional*): | |
Optional nested speaker embeddings dictionary. The first level contains voice preset names (e.g | |
`"en_speaker_4"`). The second level contains `"semantic_prompt"`, `"coarse_prompt"` and `"fine_prompt"` | |
embeddings. The values correspond to the path of the corresponding `np.ndarray`. See | |
[here](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c) for | |
a list of `voice_preset_names`. | |
""" | |
tokenizer_class = "AutoTokenizer" | |
attributes = ["tokenizer"] | |
preset_shape = { | |
"semantic_prompt": 1, | |
"coarse_prompt": 2, | |
"fine_prompt": 2, | |
} | |
def __init__(self, tokenizer, speaker_embeddings=None): | |
super().__init__(tokenizer) | |
self.speaker_embeddings = speaker_embeddings | |
def from_pretrained( | |
cls, pretrained_processor_name_or_path, speaker_embeddings_dict_path="speaker_embeddings_path.json", **kwargs | |
): | |
r""" | |
Instantiate a Bark processor associated with a pretrained model. | |
Args: | |
pretrained_model_name_or_path (`str` or `os.PathLike`): | |
This can be either: | |
- a string, the *model id* of a pretrained [`BarkProcessor`] hosted inside a model repo on | |
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or | |
namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. | |
- a path to a *directory* containing a processor saved using the [`~BarkProcessor.save_pretrained`] | |
method, e.g., `./my_model_directory/`. | |
speaker_embeddings_dict_path (`str`, *optional*, defaults to `"speaker_embeddings_path.json"`): | |
The name of the `.json` file containing the speaker_embeddings dictionnary located in | |
`pretrained_model_name_or_path`. If `None`, no speaker_embeddings is loaded. | |
**kwargs | |
Additional keyword arguments passed along to both | |
[`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`]. | |
""" | |
if speaker_embeddings_dict_path is not None: | |
speaker_embeddings_path = get_file_from_repo( | |
pretrained_processor_name_or_path, | |
speaker_embeddings_dict_path, | |
subfolder=kwargs.pop("subfolder", None), | |
cache_dir=kwargs.pop("cache_dir", None), | |
force_download=kwargs.pop("force_download", False), | |
proxies=kwargs.pop("proxies", None), | |
resume_download=kwargs.pop("resume_download", False), | |
local_files_only=kwargs.pop("local_files_only", False), | |
use_auth_token=kwargs.pop("use_auth_token", None), | |
revision=kwargs.pop("revision", None), | |
) | |
if speaker_embeddings_path is None: | |
logger.warning( | |
f"""`{os.path.join(pretrained_processor_name_or_path,speaker_embeddings_dict_path)}` does not exists | |
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json | |
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.""" | |
) | |
speaker_embeddings = None | |
else: | |
with open(speaker_embeddings_path) as speaker_embeddings_json: | |
speaker_embeddings = json.load(speaker_embeddings_json) | |
else: | |
speaker_embeddings = None | |
tokenizer = AutoTokenizer.from_pretrained(pretrained_processor_name_or_path, **kwargs) | |
return cls(tokenizer=tokenizer, speaker_embeddings=speaker_embeddings) | |
def save_pretrained( | |
self, | |
save_directory, | |
speaker_embeddings_dict_path="speaker_embeddings_path.json", | |
speaker_embeddings_directory="speaker_embeddings", | |
push_to_hub: bool = False, | |
**kwargs, | |
): | |
""" | |
Saves the attributes of this processor (tokenizer...) in the specified directory so that it can be reloaded | |
using the [`~BarkProcessor.from_pretrained`] method. | |
Args: | |
save_directory (`str` or `os.PathLike`): | |
Directory where the tokenizer files and the speaker embeddings will be saved (directory will be created | |
if it does not exist). | |
speaker_embeddings_dict_path (`str`, *optional*, defaults to `"speaker_embeddings_path.json"`): | |
The name of the `.json` file that will contains the speaker_embeddings nested path dictionnary, if it | |
exists, and that will be located in `pretrained_model_name_or_path/speaker_embeddings_directory`. | |
speaker_embeddings_directory (`str`, *optional*, defaults to `"speaker_embeddings/"`): | |
The name of the folder in which the speaker_embeddings arrays will be saved. | |
push_to_hub (`bool`, *optional*, defaults to `False`): | |
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the | |
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your | |
namespace). | |
kwargs: | |
Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. | |
""" | |
if self.speaker_embeddings is not None: | |
os.makedirs(os.path.join(save_directory, speaker_embeddings_directory, "v2"), exist_ok=True) | |
embeddings_dict = {} | |
embeddings_dict["repo_or_path"] = save_directory | |
for prompt_key in self.speaker_embeddings: | |
if prompt_key != "repo_or_path": | |
voice_preset = self._load_voice_preset(prompt_key) | |
tmp_dict = {} | |
for key in self.speaker_embeddings[prompt_key]: | |
np.save( | |
os.path.join( | |
embeddings_dict["repo_or_path"], speaker_embeddings_directory, f"{prompt_key}_{key}" | |
), | |
voice_preset[key], | |
allow_pickle=False, | |
) | |
tmp_dict[key] = os.path.join(speaker_embeddings_directory, f"{prompt_key}_{key}.npy") | |
embeddings_dict[prompt_key] = tmp_dict | |
with open(os.path.join(save_directory, speaker_embeddings_dict_path), "w") as fp: | |
json.dump(embeddings_dict, fp) | |
super().save_pretrained(save_directory, push_to_hub, **kwargs) | |
def _load_voice_preset(self, voice_preset: str = None, **kwargs): | |
voice_preset_paths = self.speaker_embeddings[voice_preset] | |
voice_preset_dict = {} | |
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]: | |
if key not in voice_preset_paths: | |
raise ValueError( | |
f"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]." | |
) | |
path = get_file_from_repo( | |
self.speaker_embeddings.get("repo_or_path", "/"), | |
voice_preset_paths[key], | |
subfolder=kwargs.pop("subfolder", None), | |
cache_dir=kwargs.pop("cache_dir", None), | |
force_download=kwargs.pop("force_download", False), | |
proxies=kwargs.pop("proxies", None), | |
resume_download=kwargs.pop("resume_download", False), | |
local_files_only=kwargs.pop("local_files_only", False), | |
use_auth_token=kwargs.pop("use_auth_token", None), | |
revision=kwargs.pop("revision", None), | |
) | |
if path is None: | |
raise ValueError( | |
f"""`{os.path.join(self.speaker_embeddings.get("repo_or_path", "/"),voice_preset_paths[key])}` does not exists | |
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset} | |
embeddings.""" | |
) | |
voice_preset_dict[key] = np.load(path) | |
return voice_preset_dict | |
def _validate_voice_preset_dict(self, voice_preset: Optional[dict] = None): | |
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]: | |
if key not in voice_preset: | |
raise ValueError(f"Voice preset unrecognized, missing {key} as a key.") | |
if not isinstance(voice_preset[key], np.ndarray): | |
raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key])}D ndarray.") | |
if len(voice_preset[key].shape) != self.preset_shape[key]: | |
raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key])}D ndarray.") | |
def __call__( | |
self, | |
text=None, | |
voice_preset=None, | |
return_tensors="pt", | |
max_length=256, | |
add_special_tokens=False, | |
return_attention_mask=True, | |
return_token_type_ids=False, | |
**kwargs, | |
): | |
""" | |
Main method to prepare for the model one or several sequences(s). This method forwards the `text` and `kwargs` | |
arguments to the AutoTokenizer's [`~AutoTokenizer.__call__`] to encode the text. The method also proposes a | |
voice preset which is a dictionary of arrays that conditions `Bark`'s output. `kwargs` arguments are forwarded | |
to the tokenizer and to `cached_file` method if `voice_preset` is a valid filename. | |
Args: | |
text (`str`, `List[str]`, `List[List[str]]`): | |
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings | |
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set | |
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences). | |
voice_preset (`str`, `Dict[np.ndarray]`): | |
The voice preset, i.e the speaker embeddings. It can either be a valid voice_preset name, e.g | |
`"en_speaker_1"`, or directly a dictionnary of `np.ndarray` embeddings for each submodel of `Bark`. Or | |
it can be a valid file name of a local `.npz` single voice preset. | |
return_tensors (`str` or [`~utils.TensorType`], *optional*): | |
If set, will return tensors of a particular framework. Acceptable values are: | |
- `'pt'`: Return PyTorch `torch.Tensor` objects. | |
- `'np'`: Return NumPy `np.ndarray` objects. | |
Returns: | |
Tuple([`BatchEncoding`], [`BatchFeature`]): A tuple composed of a [`BatchEncoding`], i.e the output of the | |
`tokenizer` and a [`BatchFeature`], i.e the voice preset with the right tensors type. | |
""" | |
if voice_preset is not None and not isinstance(voice_preset, dict): | |
if ( | |
isinstance(voice_preset, str) | |
and self.speaker_embeddings is not None | |
and voice_preset in self.speaker_embeddings | |
): | |
voice_preset = self._load_voice_preset(voice_preset) | |
else: | |
if isinstance(voice_preset, str) and not voice_preset.endswith(".npz"): | |
voice_preset = voice_preset + ".npz" | |
voice_preset = np.load(voice_preset) | |
if voice_preset is not None: | |
self._validate_voice_preset_dict(voice_preset, **kwargs) | |
voice_preset = BatchFeature(data=voice_preset, tensor_type=return_tensors) | |
encoded_text = self.tokenizer( | |
text, | |
return_tensors=return_tensors, | |
padding="max_length", | |
max_length=max_length, | |
return_attention_mask=return_attention_mask, | |
return_token_type_ids=return_token_type_ids, | |
add_special_tokens=add_special_tokens, | |
**kwargs, | |
) | |
if voice_preset is not None: | |
encoded_text["history_prompt"] = voice_preset | |
return encoded_text | |