helpers/model_init_scripts/init_dummy_model_with_encodec.py (49 lines of code) (raw):

import argparse import os from transformers import AutoConfig from parler_tts import ParlerTTSDecoderConfig, ParlerTTSForCausalLM, ParlerTTSForConditionalGeneration if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("save_directory", type=str, help="Directory where to save the model and the decoder.") args = parser.parse_args() text_model = "google-t5/t5-small" encodec_version = "facebook/encodec_24khz" t5 = AutoConfig.from_pretrained(text_model) encodec = AutoConfig.from_pretrained(encodec_version) encodec_vocab_size = encodec.codebook_size num_codebooks = 8 print("num_codebooks", num_codebooks) decoder_config = ParlerTTSDecoderConfig( vocab_size=encodec_vocab_size + 1, max_position_embeddings=2048, num_hidden_layers=4, ffn_dim=512, num_attention_heads=8, layerdrop=0.0, use_cache=True, activation_function="gelu", hidden_size=512, dropout=0.0, attention_dropout=0.0, activation_dropout=0.0, pad_token_id=encodec_vocab_size, eos_token_id=encodec_vocab_size, bos_token_id=encodec_vocab_size + 1, num_codebooks=num_codebooks, ) decoder = ParlerTTSForCausalLM(decoder_config) decoder.save_pretrained(os.path.join(args.save_directory, "decoder")) model = ParlerTTSForConditionalGeneration.from_sub_models_pretrained( text_encoder_pretrained_model_name_or_path=text_model, audio_encoder_pretrained_model_name_or_path=encodec_version, decoder_pretrained_model_name_or_path=os.path.join(args.save_directory, "decoder"), vocab_size=t5.vocab_size, ) # set the appropriate bos/pad token ids model.generation_config.decoder_start_token_id = encodec_vocab_size + 1 model.generation_config.pad_token_id = encodec_vocab_size model.generation_config.eos_token_id = encodec_vocab_size # set other default generation config params model.generation_config.max_length = int(30 * model.audio_encoder.config.frame_rate) model.generation_config.do_sample = True # True model.config.pad_token_id = encodec_vocab_size model.config.decoder_start_token_id = encodec_vocab_size + 1 model.save_pretrained(os.path.join(args.save_directory, "tiny-model"))