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| from lib import * | |
| import contextlib | |
| import io | |
| import laion_clap | |
| import torch | |
| class AudioCaptioner(torch.nn.Module): | |
| def get_dummy_token(self, batch_size: int) -> torch.Tensor: | |
| return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64) | |
| def embed_waveform(self, waveform): | |
| # compute the prefix | |
| input_dict = { | |
| 'waveform': waveform # you can add more key-values | |
| } | |
| audio_embeds = self.clap_model.model.encode_audio( | |
| input_dict, | |
| device=waveform.device | |
| ) | |
| # get BxD-dim embedding (last layer) D = 1024 -> 512 after audio projection | |
| audio_embedding = torch.nn.functional.normalize(self.clap_model.model.audio_projection(audio_embeds['embedding']), dim=-1) | |
| return audio_embedding | |
| def create_prefix(self, waveform, batch_size): | |
| if waveform is not None: | |
| audio_embedding = self.embed_waveform(waveform) | |
| else: | |
| audio_embedding = torch.zeros(batch_size, self.prefix_size).cuda() | |
| # project the prefix through map net and append it | |
| prefix_projections = self.clip_project(audio_embedding).view(-1, self.prefix_length, self.gpt_embedding_size) | |
| return prefix_projections | |
| def forward(self, tokens: torch.Tensor, waveform: torch.Tensor, mask: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = None, freeze_gpt = False): | |
| # embed the text | |
| embedding_text = self.gpt.transformer.wte(tokens) | |
| prefix_projections = self.create_prefix(waveform, tokens.shape[0]) | |
| embedding_text = torch.cat((prefix_projections, embedding_text), dim=1) | |
| # offset labels | |
| if labels is not None: | |
| dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) | |
| labels = torch.cat((dummy_token, tokens), dim=1) | |
| # push through GPT | |
| if freeze_gpt: | |
| with torch.no_grad(): | |
| out = self.gpt(inputs_embeds=embedding_text, labels=labels, attention_mask=mask) | |
| else: | |
| out = self.gpt(inputs_embeds=embedding_text, labels=labels, attention_mask=mask) | |
| return out | |
| def __init__(self, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512, | |
| num_layers: int = 8): | |
| super(AudioCaptioner, self).__init__() | |
| self.prefix_size = prefix_size | |
| self.prefix_length = prefix_length | |
| self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') | |
| self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] | |
| self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2, | |
| self.gpt_embedding_size * prefix_length)) | |
| self.clap_model = laion_clap.CLAP_Module( | |
| enable_fusion=False, | |
| amodel = 'HTSAT-base' | |
| ) | |
| with contextlib.redirect_stdout(io.StringIO()): | |
| self.clap_model.load_ckpt(ckpt = 'checkpoints/music_audioset_epoch_15_esc_90.14.pt') | |