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import argparse |
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import os |
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import jax as jnp |
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import numpy as onp |
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import torch |
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import torch.nn as nn |
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from music_spectrogram_diffusion import inference |
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from t5x import checkpoints |
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from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline |
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from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder |
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MODEL = "base_with_context" |
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def load_notes_encoder(weights, model): |
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model.token_embedder.weight = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"])) |
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model.position_encoding.weight = nn.Parameter( |
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torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False |
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) |
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for lyr_num, lyr in enumerate(model.encoders): |
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ly_weight = weights[f"layers_{lyr_num}"] |
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lyr.layer[0].layer_norm.weight = nn.Parameter( |
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torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]) |
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) |
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attention_weights = ly_weight["attention"] |
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lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T)) |
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lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T)) |
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lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T)) |
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lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T)) |
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lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"])) |
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lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) |
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lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) |
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lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T)) |
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model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"])) |
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return model |
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def load_continuous_encoder(weights, model): |
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model.input_proj.weight = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T)) |
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model.position_encoding.weight = nn.Parameter( |
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torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False |
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) |
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for lyr_num, lyr in enumerate(model.encoders): |
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ly_weight = weights[f"layers_{lyr_num}"] |
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attention_weights = ly_weight["attention"] |
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lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T)) |
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lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T)) |
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lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T)) |
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lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T)) |
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lyr.layer[0].layer_norm.weight = nn.Parameter( |
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torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]) |
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) |
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lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) |
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lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) |
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lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T)) |
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lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"])) |
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model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"])) |
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return model |
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def load_decoder(weights, model): |
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model.conditioning_emb[0].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T)) |
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model.conditioning_emb[2].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T)) |
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model.position_encoding.weight = nn.Parameter( |
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torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False |
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) |
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model.continuous_inputs_projection.weight = nn.Parameter( |
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torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T) |
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) |
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for lyr_num, lyr in enumerate(model.decoders): |
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ly_weight = weights[f"layers_{lyr_num}"] |
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lyr.layer[0].layer_norm.weight = nn.Parameter( |
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torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"]) |
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) |
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lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter( |
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torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T) |
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) |
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attention_weights = ly_weight["self_attention"] |
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lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T)) |
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lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T)) |
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lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T)) |
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lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T)) |
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attention_weights = ly_weight["MultiHeadDotProductAttention_0"] |
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lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T)) |
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lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T)) |
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lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T)) |
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lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T)) |
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lyr.layer[1].layer_norm.weight = nn.Parameter( |
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torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]) |
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) |
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lyr.layer[2].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"])) |
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lyr.layer[2].film.scale_bias.weight = nn.Parameter( |
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torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T) |
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) |
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lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) |
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lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) |
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lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T)) |
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model.decoder_norm.weight = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"])) |
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model.spec_out.weight = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T)) |
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return model |
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def main(args): |
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t5_checkpoint = checkpoints.load_t5x_checkpoint(args.checkpoint_path) |
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t5_checkpoint = jnp.tree_util.tree_map(onp.array, t5_checkpoint) |
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gin_overrides = [ |
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"from __gin__ import dynamic_registration", |
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"from music_spectrogram_diffusion.models.diffusion import diffusion_utils", |
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"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0", |
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"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()", |
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] |
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gin_file = os.path.join(args.checkpoint_path, "..", "config.gin") |
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gin_config = inference.parse_training_gin_file(gin_file, gin_overrides) |
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synth_model = inference.InferenceModel(args.checkpoint_path, gin_config) |
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scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", variance_type="fixed_large") |
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notes_encoder = SpectrogramNotesEncoder( |
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max_length=synth_model.sequence_length["inputs"], |
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vocab_size=synth_model.model.module.config.vocab_size, |
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d_model=synth_model.model.module.config.emb_dim, |
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dropout_rate=synth_model.model.module.config.dropout_rate, |
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num_layers=synth_model.model.module.config.num_encoder_layers, |
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num_heads=synth_model.model.module.config.num_heads, |
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d_kv=synth_model.model.module.config.head_dim, |
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d_ff=synth_model.model.module.config.mlp_dim, |
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feed_forward_proj="gated-gelu", |
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) |
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continuous_encoder = SpectrogramContEncoder( |
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input_dims=synth_model.audio_codec.n_dims, |
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targets_context_length=synth_model.sequence_length["targets_context"], |
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d_model=synth_model.model.module.config.emb_dim, |
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dropout_rate=synth_model.model.module.config.dropout_rate, |
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num_layers=synth_model.model.module.config.num_encoder_layers, |
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num_heads=synth_model.model.module.config.num_heads, |
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d_kv=synth_model.model.module.config.head_dim, |
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d_ff=synth_model.model.module.config.mlp_dim, |
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feed_forward_proj="gated-gelu", |
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) |
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decoder = T5FilmDecoder( |
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input_dims=synth_model.audio_codec.n_dims, |
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targets_length=synth_model.sequence_length["targets_context"], |
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max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time, |
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d_model=synth_model.model.module.config.emb_dim, |
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num_layers=synth_model.model.module.config.num_decoder_layers, |
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num_heads=synth_model.model.module.config.num_heads, |
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d_kv=synth_model.model.module.config.head_dim, |
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d_ff=synth_model.model.module.config.mlp_dim, |
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dropout_rate=synth_model.model.module.config.dropout_rate, |
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) |
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notes_encoder = load_notes_encoder(t5_checkpoint["target"]["token_encoder"], notes_encoder) |
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continuous_encoder = load_continuous_encoder(t5_checkpoint["target"]["continuous_encoder"], continuous_encoder) |
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decoder = load_decoder(t5_checkpoint["target"]["decoder"], decoder) |
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melgan = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder") |
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pipe = SpectrogramDiffusionPipeline( |
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notes_encoder=notes_encoder, |
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continuous_encoder=continuous_encoder, |
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decoder=decoder, |
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scheduler=scheduler, |
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melgan=melgan, |
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) |
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if args.save: |
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pipe.save_pretrained(args.output_path) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.") |
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parser.add_argument( |
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"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not." |
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) |
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parser.add_argument( |
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"--checkpoint_path", |
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default=f"{MODEL}/checkpoint_500000", |
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type=str, |
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required=False, |
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help="Path to the original jax model checkpoint.", |
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) |
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args = parser.parse_args() |
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main(args) |
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