TinyWave Base Speech 2B

TinyWave Base Speech 2B is a compact speech-to-speech generation model distilled from the 7B SPIRIT-LM-Base teacher. It uses HuBERT-based phonetic tokens for efficient, high-quality speech generation and is optimized for fast inference on commodity hardware.

This model focuses on generating semantically coherent speech continuations without expressive modulation (e.g., pitch/style tokens). It is ideal for low-resource speech agents, instruction-following speech bots, and embedded systems.

📖 See the TinyWave paper (arXiv:2506.23670) and demo site for more details.

🔧 Usage

This model requires SPIRIT-LM's base speech tokenizer, which uses HuBERT units without pitch/style tokens.

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_base
speech_tokenizer = spiritlm_base()

3. Inference Code (Speech-to-Speech)

from transformers import LlamaForCausalLM, AutoTokenizer
import torchaudio
import torch

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

# Load base speech tokenizer
speech_tokenizer = spiritlm_base()

def get_inference(audio_path):
    audio, _ = torchaudio.load(audio_path)
    input_values = audio.view(1, 1, -1).to(speech_tokenizer.hubert_model.device).float()
    tokens = speech_tokenizer.encode_string(input_values)
    input_ids = tokenizer(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])

4. Decode to WAV

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

🗣️ Inference Example

🎧 Basic Speech Continuation

Input: simple_prompt.wav Output: Semantically consistent speech continuation without expressive variation.

🧠 Model Details

Feature	Description
Architecture	2B parameter distilled transformer
Tokenizer	SPIRIT-LM Base (HuBERT phonetic tokens)
Input Type	Discrete HuBERT tokens only (speech-only)
Output Type	Discrete audio tokens
Teacher Model	SPIRIT-LM-Base 7B
Tasks	Speech continuation
Distillation Method	Layer-aligned (hidden states, attention, logits)

📎 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
/

speech-base-2b