Upload model

config.json

@@ -1,9 +1,14 @@
 {
+  "architectures": [
+    "SpeechLLMModel"
+  ],
   "audio_enc_dim": 1280,
   "auto_map": {
-    "AutoConfig": "config.SpeechLLMModelConfig"
+    "AutoConfig": "config.SpeechLLMModelConfig",
+    "AutoModel": "model.SpeechLLMModel"
   },
   "llm_dim": 2048,
   "model_type": "custom_model",
+  "torch_dtype": "float32",
   "transformers_version": "4.38.2"
 }

model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:565edb10ea05cf967406923b106c0309e5d50f03da191c753bbbc0875ec5f970
+size 4975727392

model-00002-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eccbd664bb9bd6ec8a066cf7b05924291540caf89c00e7856ae60d28e6e5149a
+size 3405770712

model.py

@@ -0,0 +1,101 @@
+import torch
+from torch import nn
+import torchaudio
+from transformers import PreTrainedModel, AutoModelForCausalLM, AutoTokenizer, HubertModel, AutoProcessor
+from .config import SpeechLLMModelConfig
+from peft import LoraConfig, get_peft_model
+
+class HubertXCNNEnoder(nn.Module):
+    def __init__(self, audio_enc_dim, llm_dim):
+        super().__init__()
+        self.encoder = HubertModel.from_pretrained('facebook/hubert-xlarge-ll60k')
+        for param in self.encoder.parameters():
+            param.requires_grad = False
+
+        self.cnn = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv1d(audio_enc_dim, llm_dim // 2, kernel_size=5, stride=1, padding=0),
+            nn.ReLU(),
+            nn.Conv1d(llm_dim // 2, llm_dim, kernel_size=5, stride=2, padding=0),
+            nn.ReLU(),
+            nn.Conv1d(llm_dim, llm_dim, kernel_size=3, stride=1, padding=0),
+        )
+
+    def forward(self, x):
+        x = self.encoder(x).last_hidden_state
+        x = self.cnn(x.transpose(1, 2)).transpose(1, 2)
+        return x
+
+class SpeechLLMModel(PreTrainedModel):
+    config_class = SpeechLLMModelConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.audio_processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
+        self.audio_encoder = HubertXCNNEnoder(config.audio_enc_dim, config.llm_dim)
+        self.llm_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+        self.llm_tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+
+        peft_config = LoraConfig(
+            r=4,
+            lora_alpha=8,
+            target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj'],
+            lora_dropout=0.05,
+            task_type="CAUSAL_LM",
+        )
+        self.llm_model = get_peft_model(self.llm_model, peft_config)
+
+    def encode(self, mel, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids):
+        batch_size = mel.shape[0]
+
+        with torch.no_grad():
+            speech_embeds = self.audio_encoder(mel)
+        embedder = self.llm_model.model.model.embed_tokens
+        pre_prompt_embeds = embedder(pre_tokenized_ids)
+        post_prompt_embeds = embedder(post_tokenized_ids)
+        output_prompt_embeds = embedder(output_tokenized_ids)
+
+        combined_embeds = torch.cat([pre_prompt_embeds, speech_embeds, post_prompt_embeds, output_prompt_embeds], dim=1)
+        atts = torch.ones(combined_embeds.size()[:-1], dtype=torch.long).to(combined_embeds.device)
+
+        input_token_length = pre_tokenized_ids.shape[1] + speech_embeds.shape[1] + post_tokenized_ids.shape[1]
+        label_ids = torch.cat([
+            torch.ones([batch_size, input_token_length], device=combined_embeds.device) * -100,
+            output_tokenized_ids
+        ], 1).to(combined_embeds.device).to(torch.int64)
+        return combined_embeds, atts, label_ids
+
+    def forward(self, wav_tensor, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids, attention_mask=None):
+        combined_embeds, atts, label_ids = self.encode(wav_tensor, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids)
+        outputs = self.llm_model(inputs_embeds=combined_embeds, attention_mask=attention_mask)
+        return outputs
+    
+    def generate_meta(self, audio_path, instruction="Give me the following information about the audio [Transcript]", max_new_tokens=2000):
+        pre_speech_prompt = f'''Instruction:
+{instruction}
+
+Input: 
+<speech>'''
+        post_speech_prompt = f'''</speech>
+
+Output:'''
+        output_prompt = '\n<s>'
+
+        with torch.no_grad():
+            wav_tensor, sr = torchaudio.load(audio_path)
+            wav_tensor = self.audio_processor(wav_tensor.squeeze(), return_tensors="pt", sampling_rate=16000).input_values
+
+            pre_tokenized_ids = self.llm_tokenizer(pre_speech_prompt, padding="do_not_pad", return_tensors='pt', truncation=False, add_special_tokens=False)["input_ids"]
+            post_tokenized_ids = self.llm_tokenizer(post_speech_prompt, padding="do_not_pad", return_tensors='pt', truncation=False, add_special_tokens=False)["input_ids"]
+            output_tokenized_ids = self.llm_tokenizer(output_prompt, padding="do_not_pad", return_tensors='pt', truncation=False, add_special_tokens=False)["input_ids"]
+
+            combined_embeds, atts, label_ids = self.encode(wav_tensor, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids)
+
+            out = self.llm_model.generate(
+                inputs_embeds=combined_embeds,
+                max_new_tokens=max_new_tokens,
+            ).cpu().tolist()[0]
+
+            output_text = self.llm_tokenizer.decode(out, skip_special_tokens=False)
+            return output_text
+