Upload model
Browse files- config.json +6 -1
- model-00001-of-00002.safetensors +2 -2
- model-00002-of-00002.safetensors +2 -2
- model.py +27 -21
- model.safetensors.index.json +202 -510
config.json
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{
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"audio_enc_dim": 1280,
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"audio_encoder_name": "facebook/hubert-xlarge-ll60k",
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"audio_processor_name": "facebook/hubert-large-ls960-ft",
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"auto_map": {
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"AutoConfig": "config.SpeechLLMModelConfig"
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},
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"llm_dim": 2048,
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"llm_model_checkpoint": "hf_repo/llm_model_checkpoint",
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"llm_model_name": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"model_type": "custom_model",
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"transformers_version": "4.38.2"
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}
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{
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"architectures": [
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"SpeechLLMModel"
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],
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"audio_enc_dim": 1280,
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"audio_encoder_name": "facebook/hubert-xlarge-ll60k",
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"audio_processor_name": "facebook/hubert-large-ls960-ft",
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"auto_map": {
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"AutoConfig": "config.SpeechLLMModelConfig",
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"AutoModel": "model.SpeechLLMModel"
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},
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"llm_dim": 2048,
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"llm_model_checkpoint": "hf_repo/llm_model_checkpoint",
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"llm_model_name": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"model_type": "custom_model",
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"torch_dtype": "float32",
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"transformers_version": "4.38.2"
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}
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model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:18785830f9c696dfb288148f64e92b017789e290fbfdbffa4f8c03e903ed65b2
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size 4973217960
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model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:fbc74aa8e2666342b54594419847adfa6279238c9416dda34a09202d00dd638f
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size 3395612080
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model.py
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@@ -6,12 +6,9 @@ from .config import SpeechLLMModelConfig
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from peft import LoraConfig, get_peft_model
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class HubertXCNNEnoder(nn.Module):
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def __init__(self, audio_enc_dim, llm_dim):
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super().__init__()
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self.encoder = HubertModel.from_pretrained(
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for param in self.encoder.parameters():
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param.requires_grad = False
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self.cnn = nn.Sequential(
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nn.ReLU(),
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nn.Conv1d(audio_enc_dim, llm_dim // 2, kernel_size=5, stride=1, padding=0),
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x = self.encoder(x).last_hidden_state
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x = self.cnn(x.transpose(1, 2)).transpose(1, 2)
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return x
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class SpeechLLMModel(PreTrainedModel):
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config_class = SpeechLLMModelConfig
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def __init__(self, config):
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super().__init__(config)
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self.audio_processor = AutoProcessor.from_pretrained(
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self.audio_encoder = HubertXCNNEnoder(config.audio_enc_dim, config.llm_dim)
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self.
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def encode(self, mel, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids):
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batch_size = mel.shape[0]
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with torch.no_grad():
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speech_embeds = self.audio_encoder(mel)
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embedder = self.llm_model.model.
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pre_prompt_embeds = embedder(pre_tokenized_ids)
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post_prompt_embeds = embedder(post_tokenized_ids)
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output_prompt_embeds = embedder(output_tokenized_ids)
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return outputs
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def generate_meta(self, audio_path, instruction="Give me the following information about the audio [Transcript]", max_new_tokens=2000):
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pre_speech_prompt = f'''Instruction:
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{instruction}
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post_tokenized_ids = self.llm_tokenizer(post_speech_prompt, padding="do_not_pad", return_tensors='pt', truncation=False, add_special_tokens=False)["input_ids"]
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output_tokenized_ids = self.llm_tokenizer(output_prompt, padding="do_not_pad", return_tensors='pt', truncation=False, add_special_tokens=False)["input_ids"]
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combined_embeds, atts, label_ids = self.encode(wav_tensor, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids)
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out = self.llm_model.generate(
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inputs_embeds=combined_embeds,
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max_new_tokens=max_new_tokens,
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).cpu().tolist()[0]
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output_text = self.llm_tokenizer.decode(out, skip_special_tokens=
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return output_text
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from peft import LoraConfig, get_peft_model
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class HubertXCNNEnoder(nn.Module):
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def __init__(self, audio_enc_dim, llm_dim, encoder_name):
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super().__init__()
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self.encoder = HubertModel.from_pretrained(encoder_name)
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self.cnn = nn.Sequential(
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nn.ReLU(),
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nn.Conv1d(audio_enc_dim, llm_dim // 2, kernel_size=5, stride=1, padding=0),
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x = self.encoder(x).last_hidden_state
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x = self.cnn(x.transpose(1, 2)).transpose(1, 2)
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return x
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def return_device(self):
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return next(self.parameters()).device
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class SpeechLLMModel(PreTrainedModel):
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config_class = SpeechLLMModelConfig
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def __init__(self, config):
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super().__init__(config)
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self.audio_processor = AutoProcessor.from_pretrained(config.audio_processor_name)
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self.audio_encoder = HubertXCNNEnoder(config.audio_enc_dim, config.llm_dim, config.audio_encoder_name)
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self.llm_model = AutoModelForCausalLM.from_pretrained(config.llm_model_checkpoint)
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self.llm_tokenizer = AutoTokenizer.from_pretrained(config.llm_model_name)
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# self.llm_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
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# self.llm_tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
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# peft_config = LoraConfig(
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# r=4,
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# lora_alpha=8,
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# target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj'],
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# lora_dropout=0.05,
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# task_type="CAUSAL_LM",
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# )
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# self.llm_model = get_peft_model(self.llm_model, peft_config)
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def encode(self, mel, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids):
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batch_size = mel.shape[0]
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with torch.no_grad():
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speech_embeds = self.audio_encoder(mel)
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embedder = self.llm_model.model.embed_tokens
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pre_prompt_embeds = embedder(pre_tokenized_ids)
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post_prompt_embeds = embedder(post_tokenized_ids)
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output_prompt_embeds = embedder(output_tokenized_ids)
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return outputs
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def generate_meta(self, audio_path, instruction="Give me the following information about the audio [Transcript]", max_new_tokens=2000):
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device = self.audio_encoder.return_device()
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pre_speech_prompt = f'''Instruction:
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{instruction}
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post_tokenized_ids = self.llm_tokenizer(post_speech_prompt, padding="do_not_pad", return_tensors='pt', truncation=False, add_special_tokens=False)["input_ids"]
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output_tokenized_ids = self.llm_tokenizer(output_prompt, padding="do_not_pad", return_tensors='pt', truncation=False, add_special_tokens=False)["input_ids"]
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combined_embeds, atts, label_ids = self.encode(wav_tensor.to(device), pre_tokenized_ids.to(device), post_tokenized_ids.to(device), output_tokenized_ids.to(device))
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out = self.llm_model.generate(
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inputs_embeds=combined_embeds,
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max_new_tokens=max_new_tokens,
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).cpu().tolist()[0]
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output_text = self.llm_tokenizer.decode(out, skip_special_tokens=True)
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return output_text
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model.safetensors.index.json
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{
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"metadata": {
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"total_size":
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},
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"weight_map": {
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"audio_encoder.cnn.1.bias": "model-00001-of-00002.safetensors",
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"audio_encoder.encoder.feature_projection.projection.bias": "model-00001-of-00002.safetensors",
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"audio_encoder.encoder.feature_projection.projection.weight": "model-00001-of-00002.safetensors",
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"audio_encoder.encoder.masked_spec_embed": "model-00001-of-00002.safetensors",
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| 1019 |
}
|
| 1020 |
}
|