FlexiCodec: A Dynamic Neural Audio Codec for Low Frame Rates

Abstract

Neural audio codecs are foundational to speech language models. It is expected to have a low frame rate and decoupled semantic and acoustic information. A lower frame rate codec can reduce the computational cost of speech language models by shortening the sequence length. Recent studies have developed 12.5Hz low-frame-rate audio codecs, but even lower frame rate codecs remain underexplored. We find that a major challenge for very low frame rate tokens is missing semantic information. This paper introduces FlexiCodec to address this limitation. FlexiCodec improves semantic preservation with a dynamic frame rate approach and introduces a novel architecture featuring an ASR feature-assisted dual stream encoding and Transformer bottlenecks. With dynamic frame rates, it uses less frames at information-sparse regions through adaptively merging semantically similar frames. A dynamic frame rate also allows FlexiCodec to support inference-time controllable frame rates between 3Hz and 12.5Hz. Experiments on 6.25Hz, 8.3Hz and 12.5Hz average frame rates confirm that FlexiCodec excels over baseline systems in semantic information preservation and delivers a high audio reconstruction quality. We also validate the effectiveness of FlexiCodec in language model-based TTS.

Installation

git clone https://github.com/amphionspace/FlexiCodec.git
cd FlexiCodec
pip install -r requirements.txt

FlexiCodec

To run inference (automatically downloads checkpoint from huggingface):

import torch
import torchaudio
from flexicodec.infer import prepare_model, encode_flexicodec

model_dict = prepare_model()
  
# Load a real audio file
audio_path = "YOUR_WAV.wav"
audio, sample_rate = torchaudio.load(audio_path)
with torch.no_grad():
    encoded_output = encode_flexicodec(audio, model_dict, sample_rate, num_quantizers=8, merging_threshold=0.91)
    
    reconstructed_audio = model_dict['model'].decode_from_codes(
        semantic_codes=encoded_output['semantic_codes'],
        acoustic_codes=encoded_output['acoustic_codes'],
        token_lengths=encoded_output['token_lengths'],
    )

duration = audio.shape[-1] / sample_rate
output_path = 'decoded_audio.wav'
torchaudio.save(output_path, reconstructed_audio.cpu().squeeze(1), 16000)

print(f"Saved decoded audio to {output_path}")
print(f"This sample avg frame rate: {encoded_output['token_lengths'].shape[-1] / duration:.4f} frames/sec")

Notes:

• You may tune the num_quantizers=xxx (maximum 24), merging_threshold=xxx (maximum 1.0) parameters. If you set merging_threshold=1.0, it will be a standard 12.5Hz neural audio codec. All of its token_lengths items will be 1.

• For mainland China users, you might need to execute export HF_ENDPOINT=https://hf-mirror.com in terminal, before running the code. If you don't want to automatically download from huggingface, you can manually specify your downloaded checkpoint paths in prepare_model.

• Batched input is supported. You can directly pass audios shaped [B,T] to the script above, but the audio length information will be unavailable. To resolve this, you can additionally pass an audio_lens parameter to encode_flexicodec, and you can crop the output for each audio in encoded_output[speech_token_len].

• If you want to use the above code elsewhere, you might want to add sys.path.append('/path/to/FlexiCodec') to find the code.

• To extract continuous features from the semantic tokens, use:

  feat = model_dict['model'].get_semantic_feature(encoded_output['semantic_codes'])

• Model source code is available at flexicodec/modeling_flexicodec.py.

FlexiCodec-TTS

First, install additional dependencies:

sudo apt install espeak-ng
pip install cached_path phonemizer openai-whisper

FlexiCodec-based AR+NAR TTS Inference

The AR+NAR TTS system generates speech tokens from text using an autoregressive transformer model, and then uses the Voicebox NAR system to decode the tokens into audio.

To perform complete text-to-speech with both AR generation and NAR decoding:

import torch
import torchaudio
from flexicodec.ar_tts.inference_tts import tts_synthesize
from flexicodec.ar_tts.modeling_artts import prepare_artts_model
from flexicodec.nar_tts.inference_voicebox import prepare_voicebox_model
from cached_path import cached_path

# Prepare both AR and NAR models
ar_checkpoint = cached_path('hf://jiaqili3/flexicodec/artts.safetensors')
nar_checkpoint = cached_path('hf://jiaqili3/flexicodec/nartts.safetensors')

ar_model_dict = prepare_artts_model(ar_checkpoint)
nar_model_dict = prepare_voicebox_model(nar_checkpoint)

# Full TTS synthesis
output_audio, output_sr, duration_classes = tts_synthesize(
    ar_model_dict=ar_model_dict,
    nar_model_dict=nar_model_dict,
    text="Hello, this is a complete text to speech example.",
    language="en",
    ref_audio_path="./audio_examples/1089-134686-0030.flac",  # Reference voice
    ref_text="be ware of making that mistake",  # Optional reference text
    merging_threshold=0.91,  # Frame rate control. Only two options supported: 0.91 or 0.86. If you set it to 0.91, the output is roughly 8Hz. The other option is about 6Hz.
    beam_size=1,
    top_k=25,
    temperature=1.0,
    predict_duration=True,
    duration_top_k=1,
    n_timesteps=15,  # NAR diffusion steps
    cfg=2.0,  # NAR classifier-free guidance
    rescale_cfg=0.75,  # NAR CFG rescaling
    use_nar=True,  # Set to False for AR-only decoding
)

# Save output
output_path = "output.wav"
torchaudio.save(output_path, output_audio.unsqueeze(0) if output_audio.dim() == 1 else output_audio, output_sr)

# Calculate and print frame rate
duration = output_audio.shape[-1] / output_sr
avg_frame_rate = duration_classes.shape[-1] / duration
print(f"Saved output to {output_path}")
print(f"This sample avg frame rate: {avg_frame_rate:.4f} frames/sec")

Notes:

• tts_synthesize performs the full pipeline: AR generation + NAR decoding to audio
• The function returns a tuple: (output_audio, sample_rate, duration_classes)
• duration_classes contains the token durations which can be used to calculate the average frame rate
• Reference audio (ref_audio_path) provides the voice/style characteristics
• Reference text (ref_text) is optional and can help with prosody alignment
• Set use_nar=False in tts_synthesize to use AR-only decoding (faster but lower quality)
• merging_threshold controls the frame rate: 0.91 gives ~8.3Hz, 0.86 gives ~6.25Hz

FlexiCodec-based Voicebox NAR Inference

The VoiceBox NAR system can decode FlexiCodec's RVQ-1 tokens into speech. It is used as the second stage in FlexiCodec-TTS, but can also be used standalone. To run NAR TTS inference using FlexiCodec-Voicebox:

import torch
import torchaudio
from flexicodec.nar_tts.inference_voicebox import (
    prepare_voicebox_model, 
    infer_voicebox_tts
)
from cached_path import cached_path

# Prepare VoiceBox model (loads model and vocoder)
checkpoint_path = cached_path('hf://jiaqili3/flexicodec/nartts.safetensors')
model_dict = prepare_voicebox_model(
    checkpoint_path,
    n_timesteps=15,          # Number of diffusion steps (default: 15)
    cfg=2.0,                 # Classifier-free guidance scale (default: 2.0)
    rescale_cfg=0.75,        # CFG rescaling factor (default: 0.75)
)

# Load ground truth audio (target content) and extract semantic tokens via FlexiCodec
from flexicodec.infer import prepare_model as prepare_flexicodec_model, encode_flexicodec

flexicodec_dict = prepare_flexicodec_model()
gt_audio_path = "audio_examples/1089-134686-0030.flac"  # Ground truth (target content)
gt_audio, gt_sr = torchaudio.load(gt_audio_path)

# Extract semantic tokens and length_ids from ground truth audio
with torch.no_grad():
    encoded_output = encode_flexicodec(gt_audio, flexicodec_dict, gt_sr, merging_threshold=0.9) # You can use any merging threshold value here. 
    audio_tokens = encoded_output['semantic_codes'].squeeze()  # [T] semantic token indices
    length_ids = encoded_output['token_lengths'].squeeze()     # [T] duration classes

# Load prompt audio (reference voice/style)
prompt_audio_path = "audio_examples/1089-134686-0032.flac"  # Reference audio (voice/style)
prompt_audio, _ = torchaudio.load(prompt_audio_path)

# Run VoiceBox NAR inference
output_audio, output_sr = infer_voicebox_tts(
    model_dict=model_dict,
    audio_tokens=audio_tokens,     # [T] semantic token indices from FlexiCodec
    length_ids=length_ids,         # [T] duration classes from FlexiCodec
    prompt_audio=prompt_audio,     # [1, T_audio] prompt audio tensor
    prompt_audio_path=prompt_audio_path,  # Optional: for feature caching
    framerate=1.0                  # Frame rate control (default: 1.0, max: 1.0)
                                   # Lower values (e.g., 0.87, 0.91) enable dynamic merging
)

# Save output
output_path = "output_nar.wav"
torchaudio.save(output_path, output_audio.unsqueeze(0) if output_audio.dim() == 1 else output_audio, output_sr)

# Calculate and print frame rate
duration = output_audio.shape[-1] / output_sr
avg_frame_rate = length_ids.shape[-1] / duration
print(f"Saved output to {output_path}")
print(f"This sample avg frame rate: {avg_frame_rate:.4f} frames/sec")

Notes:

• The model automatically detects and uses CUDA, MPS (Apple Silicon), or CPU devices
• audio_tokens are semantic token indices extracted from ground truth audio via FlexiCodec encoding (as shown above) or generated by an AR model
• length_ids are duration classes for each token extracted from FlexiCodec encoding (optional, defaults to 1 for each token)
• prompt_audio determines the voice/style characteristics of the output
• The ground truth audio determines the semantic content of the output through its extracted tokens
• Output sample rate is typically 16000 Hz or 24000 Hz depending on the model configuration
• You can reuse model_dict for multiple inference calls to avoid reloading the model
• framerate controls FlexiCodec's dynamic frame rate: lower values (e.g., 0.87, 0.91) enable merging for lower average frame rates, while 1.0 disables merging (standard 12.5Hz)

Training reference implementations

• For FlexiCodec: see https://github.com/jiaqili3/flexicodec_training_share
• For FlexiCodec-TTS: Inside flexicodec/ar_tts/modeling_artts.py and flexicodec/nar_tts/modeling_voicebox.py there are training_forward methods that receive audios and prepared sensevoice-small input "FBank" features. (dl_output dictionary containing x (the feature_extractor output), x_lens (length of each x before padding), audio (the 16khz audio tensor)). Training can be replicated by passing the same data to the training_forward methods.

If you need more code for training FlexiCodec-TTS, you can contact me or create an issue.

Acknowledgements & Citation

• Our codebase setup is based on DualCodec
• We thank the Mimi Codec for transformer implementations

If you find our works useful, please consider citing as:

@article{li2025flexicodec,
  title={FlexiCodec: A Dynamic Neural Audio Codec for Low Frame Rates},
  author={Li, Jiaqi and Qian, Yao and Hu, Yuxuan and Zhang, Leying and Wang, Xiaofei and Lu, Heng and Thakker, Manthan and Li, Jinyu and Zhao, Shang and Wu, Zhizheng},
  journal={arXiv preprint arXiv:2510.00981},
  year={2025}
}

@article{li2025dualcodec,
  title={Dualcodec: A low-frame-rate, semantically-enhanced neural audio codec for speech generation},
  author={Li, Jiaqi and Lin, Xiaolong and Li, Zhekai and Huang, Shixi and Wang, Yuancheng and Wang, Chaoren and Zhan, Zhenpeng and Wu, Zhizheng},
  journal={Interspeech 2025},
  year={2025}
}