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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 | |