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--- |
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datasets: |
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- jan-hq/instruction-speech-v1 |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sound language model |
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--- |
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## Model Details |
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We have developed and released the family [Jan-Llama3](https://huggingface.co/collections/jan-hq/jan-llama3-668e4dad446c8736208dca4f). This family is natively understanding audio and text input. |
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We continue to expand [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with sound understanding capabilities by leveraging 700M tokens [Instruction Speech v1](https://huggingface.co/datasets/jan-hq/instruction-speech-v1) dataset. |
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**Model developers** Homebrew Research. |
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**Input** Text and sound. |
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**Output** Text. |
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**Model Architecture** Llama-3. |
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**Language(s):** English. |
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## Intended Use |
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**Intended Use Cases** This family is primarily intended for research applications. This version aims to further improve the LLM on sound understanding capabilities. |
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**Out-of-scope** The use of Llama-3-Sound in any manner that violates applicable laws or regulations is strictly prohibited. |
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## How to Get Started with the Model |
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First, we need to convert the audio file to sound tokens |
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```python |
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import torch |
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import torchaudio |
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from encodec import EncodecModel |
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from encodec.utils import convert_audio |
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def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device="cuda"): |
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# Initialize Encodec |
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model = EncodecModel.encodec_model_24khz() |
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model.set_target_bandwidth(target_bandwidth) |
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model.to(device) |
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# Load and preprocess audio |
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wav, sr = torchaudio.load(audio_path) |
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wav = convert_audio(wav, sr, model.sample_rate, model.channels) |
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wav = wav.unsqueeze(0).to(device) |
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# Encode audio |
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with torch.no_grad(): |
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encoded_frames = model.encode(wav) |
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codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1) |
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# Flatten codes |
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audio_code1, audio_code2 = codes[0][0], codes[0][1] |
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flatten_tokens = torch.stack((audio_code1, audio_code2), dim=1).flatten().tolist() |
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# Convert to sound tokens |
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result = ''.join(f'<|sound_{num}|>' for num in flatten_tokens) |
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return f'<|sound_start|>{result}<|sound_end|>' |
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# Usage |
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sound_tokens = audio_to_sound_tokens("/path/to/your/audio/file") |
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``` |
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Then, we can inference the model the same as any other LLM. |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline |
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def setup_pipeline(model_path, use_4bit=True): |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model_kwargs = {"device_map": "auto"} |
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if use_4bit: |
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model_kwargs["quantization_config"] = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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) |
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model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs) |
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return pipeline("text-generation", model=model, tokenizer=tokenizer) |
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def generate_text(pipe, messages, max_new_tokens=64, temperature=0.0, do_sample=False): |
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generation_args = { |
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"max_new_tokens": max_new_tokens, |
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"return_full_text": False, |
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"temperature": temperature, |
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"do_sample": do_sample, |
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} |
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output = pipe(messages, **generation_args) |
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return output[0]['generated_text'] |
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# Usage |
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llm_path = "jan-hq/Jan-Llama3-0708" |
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pipe = setup_pipeline(llm_path, use_4bit=True) |
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messages = [ |
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{"role": "user", "content": sound_tokens}, |
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] |
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generated_text = generate_text(pipe, messages) |
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print(generated_text) |
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``` |
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## Training process |
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**Training Metrics Image**: Below is a snapshot of the training loss curve visualized. |
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### Hardware |
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**GPU Configuration**: Cluster of 8x NVIDIA H100-SXM-80GB. |
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**GPU Usage**: |
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- **Continual Training**: 8 hours. |
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### Training Arguments |
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| Parameter | Continual Training | |
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|----------------------------|-------------------------| |
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| **Epoch** | 1 | |
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| **Global batch size** | 128 | |
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| **Learning Rate** | 5e-5 | |
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| **Learning Scheduler** | Cosine with warmup | |
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| **Optimizer** | [Adam-mini](https://arxiv.org/abs/2406.16793) | |
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| **Warmup Ratio** | 0.1 | |
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| **Weight Decay** | 0.01 | |
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| **beta1** | 0.9 | |
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| **beta2** | 0.98 | |
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| **epsilon** | 1e-6 | |
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| **Gradient Cliping** | 1.0 | |
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### |
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Accelerate FSDP Config |
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``` |
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compute_environment: LOCAL_MACHINE |
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debug: false |
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distributed_type: FSDP |
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downcast_bf16: 'no' |
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enable_cpu_affinity: true |
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fsdp_config: |
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fsdp_activation_checkpointing: true |
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fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP |
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fsdp_backward_prefetch: BACKWARD_PRE |
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fsdp_cpu_ram_efficient_loading: true |
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fsdp_forward_prefetch: false |
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fsdp_offload_params: false |
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fsdp_sharding_strategy: FULL_SHARD |
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fsdp_state_dict_type: SHARDED_STATE_DICT |
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fsdp_sync_module_states: true |
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fsdp_use_orig_params: false |
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machine_rank: 0 |
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main_training_function: main |
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mixed_precision: bf16 |
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num_machines: 1 |
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num_processes: 8 |
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rdzv_backend: static |
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same_network: true |
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tpu_env: [] |
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tpu_use_cluster: false |
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tpu_use_sudo: false |
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use_cpu: false |
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``` |
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## Examples |
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1. Good example: |
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``` |
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# Question: |
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Does the following review have a positive or negative opinion of the movie? |
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i thought the relationships were wonderful , |
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Options: A). negative; B). positive; |
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# Expected Output: |
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The review has a B). positive opinion about the movie because they think the relationships were wonderful. |
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# Model's Output: |
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The review is positive. |
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``` |
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2. Misunderstanding example: |
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``` |
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# Question: |
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Translate the following sentence to Russian: |
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6 Work in JOBS and careers after 40 |
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Russian: |
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# Expected Output: |
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6 Работать в РАБОТАХ и карьере после 40 |
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Russian (Cyrillic): 6 Работать в РАБОТАХ и карьере после 40 |
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Let me explain this to you like you're five years old: This sentence means that people can still work and have careers, even if they're older than 40 years old. The words are in a different language, called Russian. |
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# Model's Output: |
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В случае, когда вы хотите перевести фразу "The first step is to identify the language of the text." на русский язык, вы можете использовать следующую фразу: "Первый шаг - определить язык текста." |
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``` |
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3. Off-tracked example: |
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``` |
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# Quesion: |
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Answer the following question: - The roots of the growing plants start to break up the rock - The plant acids dissolve the rock - The rock is broken down into smaller pieces - Erosion begins. What might be the first step of the process? |
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Answer: |
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# Expected Output: |
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The first step of the process might be: The roots of the growing plants start to break up the rock. |
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# Model's Output: |
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Step 1: Identify the key elements in the premise and hypothesis. |
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Premise: "A man in a blue shirt and a red hat is holding a sign." |
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Key elements: man, blue shirt, red hat, holding, sign |
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Hypothesis: "A man is holding a sign." |
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Key elements: man, holding, sign |
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Step 2: Compare the key elements in the premise and hypothesis. |
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In the premise, we have a man wearing a blue shirt and a red hat, while in the hypothesis, we only have a man. The premise also mentions that the man is holding a sign, which is also mentioned in the hypothesis. |
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Step 3: Determine if the hypothesis is entailed by the premise. |
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Since the hypothesis states that a man is holding a sign, and the premise confirms that a man is holding a sign, we can conclude that the hypothesis is entailed by the premise. The additional information about the man's clothing in the premise does not contradict or negate the |
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``` |
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Despite being undertrained, the model demonstrates an emerging grasp of sound-text semantics. |
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## Citation Information |
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**BibTeX:** |
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``` |
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@article{Llama-3-Sound: Sound Instruction LLM 2024, |
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title={Llama-3-Sound}, |
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author={Homebrew Research}, |
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year=2024, |
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month=July}, |
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url={https://huggingface.co/jan-hq/Jan-Llama3-0708} |
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``` |
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## Acknowledgement |
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- **[WhisperSpeech](https://github.com/collabora/WhisperSpeech)** |
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- **[Encodec](https://github.com/facebookresearch/encodec)** |
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- **[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)** |
