Update README.md

README.md

@@ -1,10 +1,16 @@
 ---
 pipeline_tag: text-to-speech
+datasets:
+- facebook/multilingual_librispeech
+- parler-tts/mls_eng
+language:
+- en
 ---
 # FlexiCodec: A Dynamic Neural Audio Codec for Low Frame Rates
 
 [![Demo Page](https://img.shields.io/badge/GitHub.io-Demo_Page-blue?logo=Github&style=flat-square)](https://flexicodec.github.io/)
-[![ArXiv](https://img.shields.io/badge/arxiv-PDF-green?logo=arxiv&style=flat-square)](https://arxiv.org/abs/2510.00981)
+[![ArXiv](https://img.shields.io/badge/arXiv-PDF-green?logo=arxiv&style=flat-square)](https://arxiv.org/abs/2510.00981)
+
 
 ## 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.
@@ -48,24 +54,143 @@ 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")
 ```
-For Chinese 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`. 
+
+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 [![Huggingface](https://img.shields.io/badge/huggingface-yellow?logo=huggingface&style=flat-square)](https://huggingface.co/jiaqili3/flexicodec/tree/main) 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.
+- 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_REPOSITORY')` to find the code.
+- 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:
+  ```python
+  feat = model_dict['model'].get_semantic_feature(encoded_output['semantic_codes'])
+  ```
+
+## FlexiCodec-TTS
+First, install additional dependencies:
+```bash
+sudo apt install espeak-ng
+pip install cached_path phonemizer openai-whisper
+```
+
+### 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:
+
+```python
+import torch
+import torchaudio
+from flexicodec.nar_tts.inference_voicebox import (
+    prepare_voicebox_model, 
+    infer_voicebox_tts
+)
+import cached_path
+# Prepare model (loads model and vocoder)
+checkpoint_path = cached_path('hf://jiaqili3/flexicodec/nartts.safetensors')
+model_dict = prepare_voicebox_model(checkpoint_path)
+
+# Option 1: Inference with audio file paths
+gt_audio_path = "audio_examples/61-70968-0000_gt.wav"  # Target content. Example GT audio
+ref_audio_path = "audio_examples/61-70968-0000_ref.wav"     # Reference voice/style. 
+
+output_audio, output_sr = infer_voicebox_tts(
+    model_dict=model_dict,
+    gt_audio_path=gt_audio_path,
+    ref_audio_path=ref_audio_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)
+    merging_threshold=1.0    # Merging threshold for frame rate control (default: 1.0, max: 1.0)
+)
+
+# Save output
+torchaudio.save("output.wav", output_audio.unsqueeze(0) if output_audio.dim() == 1 else output_audio, output_sr)
+
+# Option 2: Inference with audio tensors
+gt_audio, gt_sr = torchaudio.load("path/to/ground_truth.wav")
+ref_audio, ref_sr = torchaudio.load("path/to/reference.wav")
+
+output_audio, output_sr = infer_voicebox_tts(
+    model_dict=model_dict,
+    gt_audio=gt_audio,
+    ref_audio=ref_audio,
+    gt_sample_rate=gt_sr,
+    ref_sample_rate=ref_sr,
+    n_timesteps=15,
+    cfg=2.0,
+    rescale_cfg=0.75,
+    merging_threshold=1.0
+)
+```
+
+**Notes:**
+- The model automatically detects and uses CUDA, MPS (Apple Silicon), or CPU devices
+- Ground truth audio (`gt_audio`) determines the semantic content of the output
+- Reference audio (`ref_audio`) determines the voice/style characteristics
+- 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
+- `merging_threshold` 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)
+
+### 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:
 
-To extract continuous features from the semantic tokens, use:
 ```python
-feat = model_dict['model'].get_semantic_feature(encoded_output['semantic_codes'])
+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
+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 = 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/61-70968-0000_ref.wav",  # Reference voice
+    ref_text="bear us escort so far as the Sheriff's house",  # Optional reference text
+    merging_threshold=0.91,  # Frame rate control (used for both AR and NAR)
+    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
+torchaudio.save("output.wav", output_audio.unsqueeze(0) if output_audio.dim() == 1 else output_audio, output_sr)
 ```
 
-## FlexiCodec-TTS
-Our code for Flexicodec-based AR TTS is available at [`flexicodec/ar_tts/modeling_artts.py`](flexicodec/ar_tts/modeling_artts.py). The training step is inside `training_forward` method. It receives a `dl_output` dictionary containing `x` (the [`feature_extractor`](flexicodec/infer.py#L50) output), `x_lens` (length of each x before padding), `audio` (the 16khz audio tensor). The inference is at the `inference` method in the same file.
+**Notes:**
+- `tts_synthesize` performs the full pipeline: AR generation + NAR decoding to audio
+- 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)
+
+### Training reference implementations
+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`](flexicodec/infer.py#L50) 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. 
+
 
-Our code for Flow matching-based NAR TTS is based on the voicebox-based implementation [here](https://github.com/jiaqili3/DualCodec/tree/main/dualcodec/model_tts/voicebox).
-We plan to release TTS trained models and TTS training examples.
 
 ## Acknowledgements & Citation
 - Our codebase setup is based on [DualCodec](https://github.com/jiaqili3/DualCodec)