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| """Convert Bark checkpoint.""" | |
| import argparse | |
| import os | |
| from pathlib import Path | |
| import torch | |
| from bark.generation import _load_model as _bark_load_model | |
| from huggingface_hub import hf_hub_download | |
| from transformers import EncodecConfig, EncodecModel, set_seed | |
| from transformers.models.bark.configuration_bark import ( | |
| BarkCoarseConfig, | |
| BarkConfig, | |
| BarkFineConfig, | |
| BarkSemanticConfig, | |
| ) | |
| from transformers.models.bark.generation_configuration_bark import ( | |
| BarkCoarseGenerationConfig, | |
| BarkFineGenerationConfig, | |
| BarkGenerationConfig, | |
| BarkSemanticGenerationConfig, | |
| ) | |
| from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel | |
| from transformers.utils import logging | |
| logging.set_verbosity_info() | |
| logger = logging.get_logger(__name__) | |
| set_seed(770) | |
| new_layer_name_dict = { | |
| "c_attn": "att_proj", | |
| "c_proj": "out_proj", | |
| "c_fc": "in_proj", | |
| "transformer.": "", | |
| "h.": "layers.", | |
| "ln_1": "layernorm_1", | |
| "ln_2": "layernorm_2", | |
| "ln_f": "layernorm_final", | |
| "wpe": "position_embeds_layer", | |
| "wte": "input_embeds_layer", | |
| } | |
| REMOTE_MODEL_PATHS = { | |
| "text_small": { | |
| "repo_id": "suno/bark", | |
| "file_name": "text.pt", | |
| }, | |
| "coarse_small": { | |
| "repo_id": "suno/bark", | |
| "file_name": "coarse.pt", | |
| }, | |
| "fine_small": { | |
| "repo_id": "suno/bark", | |
| "file_name": "fine.pt", | |
| }, | |
| "text": { | |
| "repo_id": "suno/bark", | |
| "file_name": "text_2.pt", | |
| }, | |
| "coarse": { | |
| "repo_id": "suno/bark", | |
| "file_name": "coarse_2.pt", | |
| }, | |
| "fine": { | |
| "repo_id": "suno/bark", | |
| "file_name": "fine_2.pt", | |
| }, | |
| } | |
| CUR_PATH = os.path.dirname(os.path.abspath(__file__)) | |
| default_cache_dir = os.path.join(os.path.expanduser("~"), ".cache") | |
| CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0") | |
| def _get_ckpt_path(model_type, use_small=False): | |
| key = model_type | |
| if use_small: | |
| key += "_small" | |
| return os.path.join(CACHE_DIR, REMOTE_MODEL_PATHS[key]["file_name"]) | |
| def _download(from_hf_path, file_name): | |
| os.makedirs(CACHE_DIR, exist_ok=True) | |
| hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR) | |
| def _load_model(ckpt_path, device, use_small=False, model_type="text"): | |
| if model_type == "text": | |
| ModelClass = BarkSemanticModel | |
| ConfigClass = BarkSemanticConfig | |
| GenerationConfigClass = BarkSemanticGenerationConfig | |
| elif model_type == "coarse": | |
| ModelClass = BarkCoarseModel | |
| ConfigClass = BarkCoarseConfig | |
| GenerationConfigClass = BarkCoarseGenerationConfig | |
| elif model_type == "fine": | |
| ModelClass = BarkFineModel | |
| ConfigClass = BarkFineConfig | |
| GenerationConfigClass = BarkFineGenerationConfig | |
| else: | |
| raise NotImplementedError() | |
| model_key = f"{model_type}_small" if use_small else model_type | |
| model_info = REMOTE_MODEL_PATHS[model_key] | |
| if not os.path.exists(ckpt_path): | |
| logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.") | |
| _download(model_info["repo_id"], model_info["file_name"]) | |
| checkpoint = torch.load(ckpt_path, map_location=device) | |
| # this is a hack | |
| model_args = checkpoint["model_args"] | |
| if "input_vocab_size" not in model_args: | |
| model_args["input_vocab_size"] = model_args["vocab_size"] | |
| model_args["output_vocab_size"] = model_args["vocab_size"] | |
| del model_args["vocab_size"] | |
| # convert Bark model arguments to HF Bark model arguments | |
| model_args["num_heads"] = model_args.pop("n_head") | |
| model_args["hidden_size"] = model_args.pop("n_embd") | |
| model_args["num_layers"] = model_args.pop("n_layer") | |
| model_config = ConfigClass(**checkpoint["model_args"]) | |
| model = ModelClass(config=model_config) | |
| model_generation_config = GenerationConfigClass() | |
| model.generation_config = model_generation_config | |
| state_dict = checkpoint["model"] | |
| # fixup checkpoint | |
| unwanted_prefix = "_orig_mod." | |
| for k, v in list(state_dict.items()): | |
| if k.startswith(unwanted_prefix): | |
| # replace part of the key with corresponding layer name in HF implementation | |
| new_k = k[len(unwanted_prefix) :] | |
| for old_layer_name in new_layer_name_dict: | |
| new_k = new_k.replace(old_layer_name, new_layer_name_dict[old_layer_name]) | |
| state_dict[new_k] = state_dict.pop(k) | |
| extra_keys = set(state_dict.keys()) - set(model.state_dict().keys()) | |
| extra_keys = {k for k in extra_keys if not k.endswith(".attn.bias")} | |
| missing_keys = set(model.state_dict().keys()) - set(state_dict.keys()) | |
| missing_keys = {k for k in missing_keys if not k.endswith(".attn.bias")} | |
| if len(extra_keys) != 0: | |
| raise ValueError(f"extra keys found: {extra_keys}") | |
| if len(missing_keys) != 0: | |
| raise ValueError(f"missing keys: {missing_keys}") | |
| model.load_state_dict(state_dict, strict=False) | |
| n_params = model.num_parameters(exclude_embeddings=True) | |
| val_loss = checkpoint["best_val_loss"].item() | |
| logger.info(f"model loaded: {round(n_params/1e6,1)}M params, {round(val_loss,3)} loss") | |
| model.eval() | |
| model.to(device) | |
| del checkpoint, state_dict | |
| return model | |
| def load_model(pytorch_dump_folder_path, use_small=False, model_type="text"): | |
| if model_type not in ("text", "coarse", "fine"): | |
| raise NotImplementedError() | |
| device = "cpu" # do conversion on cpu | |
| ckpt_path = _get_ckpt_path(model_type, use_small=use_small) | |
| model = _load_model(ckpt_path, device, model_type=model_type, use_small=use_small) | |
| # load bark initial model | |
| bark_model = _bark_load_model(ckpt_path, "cpu", model_type=model_type, use_small=use_small) | |
| if model_type == "text": | |
| bark_model = bark_model["model"] | |
| if model.num_parameters(exclude_embeddings=True) != bark_model.get_num_params(): | |
| raise ValueError("initial and new models don't have the same number of parameters") | |
| # check if same output as the bark model | |
| batch_size = 5 | |
| sequence_length = 10 | |
| if model_type in ["text", "coarse"]: | |
| vec = torch.randint(256, (batch_size, sequence_length), dtype=torch.int) | |
| output_old_model = bark_model(vec)[0] | |
| output_new_model_total = model(vec) | |
| # take last logits | |
| output_new_model = output_new_model_total.logits[:, [-1], :] | |
| else: | |
| prediction_codeboook_channel = 3 | |
| n_codes_total = 8 | |
| vec = torch.randint(256, (batch_size, sequence_length, n_codes_total), dtype=torch.int) | |
| output_new_model_total = model(prediction_codeboook_channel, vec) | |
| output_old_model = bark_model(prediction_codeboook_channel, vec) | |
| output_new_model = output_new_model_total.logits | |
| # output difference should come from the difference of self-attention implementation design | |
| if output_new_model.shape != output_old_model.shape: | |
| raise ValueError("initial and new outputs don't have the same shape") | |
| if (output_new_model - output_old_model).abs().max().item() > 1e-3: | |
| raise ValueError("initial and new outputs are not equal") | |
| Path(pytorch_dump_folder_path).mkdir(exist_ok=True) | |
| model.save_pretrained(pytorch_dump_folder_path) | |
| def load_whole_bark_model( | |
| semantic_path, | |
| coarse_path, | |
| fine_path, | |
| append_text, | |
| hub_path, | |
| folder_path, | |
| ): | |
| pytorch_dump_folder_path = os.path.join(folder_path, append_text) | |
| semanticConfig = BarkSemanticConfig.from_pretrained(os.path.join(semantic_path, "config.json")) | |
| coarseAcousticConfig = BarkCoarseConfig.from_pretrained(os.path.join(coarse_path, "config.json")) | |
| fineAcousticConfig = BarkFineConfig.from_pretrained(os.path.join(fine_path, "config.json")) | |
| codecConfig = EncodecConfig.from_pretrained("facebook/encodec_24khz") | |
| semantic = BarkSemanticModel.from_pretrained(semantic_path) | |
| coarseAcoustic = BarkCoarseModel.from_pretrained(coarse_path) | |
| fineAcoustic = BarkFineModel.from_pretrained(fine_path) | |
| codec = EncodecModel.from_pretrained("facebook/encodec_24khz") | |
| bark_config = BarkConfig.from_sub_model_configs( | |
| semanticConfig, coarseAcousticConfig, fineAcousticConfig, codecConfig | |
| ) | |
| bark_generation_config = BarkGenerationConfig.from_sub_model_configs( | |
| semantic.generation_config, coarseAcoustic.generation_config, fineAcoustic.generation_config | |
| ) | |
| bark = BarkModel(bark_config) | |
| bark.semantic = semantic | |
| bark.coarse_acoustics = coarseAcoustic | |
| bark.fine_acoustics = fineAcoustic | |
| bark.codec_model = codec | |
| bark.generation_config = bark_generation_config | |
| Path(pytorch_dump_folder_path).mkdir(exist_ok=True) | |
| bark.save_pretrained(pytorch_dump_folder_path, repo_id=hub_path, push_to_hub=True) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| # Required parameters | |
| parser.add_argument("model_type", type=str, help="text, coarse or fine.") | |
| parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") | |
| parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.") | |
| args = parser.parse_args() | |
| load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small) | |