Llama 3 Typhoon V1.5 8b Audio Preview

🚀 Typhoon-Audio Preview

Typhoon-Audio Preview is a Thai audio-language model that supports text and audio input and text output. It's a research preview version as part of multimodal efforts.

🚀 Quick Start

llama-3-typhoon-v1.5-8b-audio-preview is a 🇹🇭 Thai audio-language model. It natively supports both text and audio input modalities, while the output is text. This version (August 2024) is our first audio-language model as part of our multimodal effort, and it is a research preview version. The base language model is our llama-3-typhoon-v1.5-8b-instruct.

More details can be found in our technical report. To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name.

✨ Features

• Model type: The LLM is based on Typhoon-1.5-8b-instruct, and the audio encoder is based on Whisper's encoder and BEATs.
• Requirement: transformers 4.38.0 or newer.
• Primary Language(s): Thai 🇹🇭 and English 🇺🇸
• Demo: https://audio.opentyphoon.ai/
• License: Llama 3 Community License

💻 Usage Examples

Basic Usage

from transformers import AutoModel
import soundfile as sf
import librosa

# Initialize from the trained model
model = AutoModel.from_pretrained(
    "scb10x/llama-3-typhoon-v1.5-8b-audio-preview", 
    torch_dtype=torch.float16,
    trust_remote_code=True
)
model.to("cuda")
model.eval()

# read a wav file (it needs to be in 16 kHz and clipped to 30 seconds)
audio, sr = sf.read("path_to_your_audio.wav")
if len(audio.shape) == 2:
    audio = audio[:, 0]
if len(audio) > 30 * sr:
    audio = audio[: 30 * sr]
if sr != 16000:
    audio = librosa.resample(audio, orig_sr=sr, target_sr=16000, res_type="fft")

# Run generation
prompt_pattern="<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n<Speech><SpeechHere></Speech> {}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
response = model.generate(
    audio=audio,
    prompt="transcribe this audio",
    prompt_pattern=prompt_pattern,
    do_sample=False,
    max_new_tokens=512,
    repetition_penalty=1.1,
    num_beams=1,
    # temperature=0.4,
    # top_p=0.9,
)
print(response)

Advanced Usage

# Generation Parameters:
# audio -- audio input, e.g., using `soundfile.read` or `librosa.resample` to read a wav file like the example above
# prompt (`str`) -- Text input to the model
# prompt_pattern (`str`) -- Chat template that is augmented with special tokens, and it must be set the same as one during training
# max_new_tokens (`int`, *optional*, defaults to 1024)
# num_beams (`int`, *optional*, defaults to 4)
# do_sample (`bool`, *optional*, defaults to True)
# top_p (`float`, *optional*, defaults to 0.9)
# repetition_penalty (`float`, *optional*, defaults to 1.0),
# length_penalty (`float`, *optional*, defaults to 1.0),
# temperature (`float`, *optional*, defaults to 1.0),

# This is also `model.generate_stream()` for streaming generation. Please refer to `modeling_typhoonaudio.py` for this function.

📚 Documentation

More information is provided in our technical report.

🔧 Technical Details

This model is experimental and may not always follow human instructions accurately, making it prone to generating hallucinations. Additionally, the model lacks moderation mechanisms and may produce harmful or inappropriate responses. Developers should carefully assess potential risks based on their specific applications.

⚠️ Important Note

This model is experimental and may not always follow human instructions accurately, making it prone to generating hallucinations. Additionally, the model lacks moderation mechanisms and may produce harmful or inappropriate responses. Developers should carefully assess potential risks based on their specific applications.

📄 License

The model is released under the Llama 3 Community License.

Follow us & Support

• https://twitter.com/opentyphoon
• https://discord.gg/us5gAYmrxw

Acknowledgements

We would like to thank the SALMONN team for open-sourcing their code and data, and thanks to the Biomedical and Data Lab at Mahidol University for releasing the fine-tuned Whisper that allowed us to adopt its encoder. Thanks to many other open-source projects for their useful knowledge sharing, data, code, and model weights.

Typhoon Team

Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Warit Sirichotedumrong, Adisai Na-Thalang, Phatrasek Jirabovonvisut, Parinthapat Pengpun, Krisanapong Jirayoot, Pathomporn Chokchainant, Kasima Tharnpipitchai, Kunat Pipatanakul