TinyWave Interleaved Expressive 2B

TinyWave Interleaved Expressive 2B is a compact, expressive speech-to-speech and speech-text language model distilled from the 7B SPIRIT-LM teacher. It supports interleaved audio and text inputs and is trained on 50k hours of public data using a multi-level layer-aligned distillation framework.

Despite being 3× smaller than its teacher, the model retains 93–97% of its accuracy on expressive benchmarks like StoryCloze and SALMon, and outperforms size-matched baselines. This model is ideal for real-time multimodal agents, spoken dialogue systems, and low-resource deployment.

📖 For more information, see the TinyWave paper (arXiv:2506.23670) and project website.

🔧 Usage

This model accepts interleaved speech and text inputs. It expects inputs to be encoded using SPIRIT-LM’s expressive speech tokenizer.

1. Clone SPIRIT-LM and Install Requirements

git clone https://github.com/facebookresearch/spiritlm
cd spiritlm
pip install -e '.[eval]'

2. Load Tokenizer

from spiritlm.speech_tokenizer import spiritlm_expressive
speech_tokenizer = spiritlm_expressive()

3. Inference Code

from transformers import LlamaForCausalLM, AutoTokenizer
import torchaudio
import torch

# Load model and tokenizer
MODEL_PATH = "tinywave/interleaved-expressive-2b"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = LlamaForCausalLM.from_pretrained(MODEL_PATH, device_map="auto", torch_dtype=torch.bfloat16)

# Audio + Speech tokenizer
speech_tokenizer = spiritlm_expressive()

def get_inference(input_audio_path):
    audio, _ = torchaudio.load(input_audio_path)
    input_values = audio.view(1, 1, -1).to(speech_tokenizer.hubert_model.device).float()
    string_tokens = speech_tokenizer.encode_string(input_values)
    input_ids = tokenizer(string_tokens, return_tensors="pt").input_ids.to(model.device)
    output = model.generate(input_ids, max_new_tokens=256, top_p=0.9, temperature=0.9, do_sample=True)
    return tokenizer.decode(output[0])

# Text-based prompt
def get_inference_text(prompt):
    input_ids = tokenizer(prompt + " [Speech]", return_tensors="pt").input_ids.to(model.device)
    output = model.generate(input_ids, max_new_tokens=256, top_p=0.9, temperature=0.9, do_sample=True)
    return tokenizer.decode(output[0])

4. Decoding to WAV (optional)

import numpy as np
from scipy.io.wavfile import write

def save_array_to_wav_int16(audio_array: np.ndarray, sampling_rate=16000, filename="output.wav"):
    scaled = np.int16(audio_array / np.max(np.abs(audio_array)) * 32767)
    write(filename, sampling_rate, scaled)

decoded_audio = speech_tokenizer.decode(output_text.replace(" ", "").replace("<s>", "").replace("</s>", ""), speaker_id=2)
save_array_to_wav_int16(decoded_audio, filename="generated.wav")

🗣️ Inference Examples

🎧 Speech Continuation

Input: speech.wav (spoken sentence) Output: Expressive speech continuation in the same style and tone.

💬 Mixed Input: Text → Speech

Prompt:

"Once upon a time in a small village, a mysterious sound echoed through the forest. [Speech]"

Output: Expressive spoken continuation in WAV format.

🧠 Model Details

Feature	Description
Architecture	2B parameter distilled transformer
Tokenizer	SPIRIT-LM Expressive (HuBERT + pitch/style)
Tasks	Speech continuation, mixed speech-text generation
Teacher Model	SPIRIT-LM-Expressive 7B
Distillation Method	Layer-aligned: hidden states, attention, logits
Input Types	Discrete HuBERT tokens and text

📎 Citation

@article{nouriborji2025tinywave,
  title={Efficient Interleaved Speech Modeling through Knowledge Distillation},
  author={Nouriborji, Mohammadmahdi and Rohanian, Morteza},
  journal={arXiv preprint arXiv:2506.23670},
  year={2025}
}

tinywave
/

interleaved-expressive-2b