Update README.md

README.md

@@ -1,12 +1,10 @@
 ---
-language:
-- en  
 license: apache-2.0
 tags:
 - audio
 - speech
 - audio-to-audio
-- speechlm
+- speech-language-models
 datasets:
 - amphion/Emilia-Dataset
 - facebook/multilingual_librispeech
@@ -14,40 +12,25 @@ datasets:
 - CSTR-Edinburgh/vctk
 - google/fleurs
 - mozilla-foundation/common_voice_13_0
-metrics:
-- wer 
-
 ---
 
-# Model Card for NeuCodec 
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-NeuCodec is an FSQ-based audio codec for speech tokenization.
-
-## Model Details
-
-<!-- Provide a longer summary of what this model is. -->
+# Model Details
 
-NeuCodec is an ultra low bit-rate audio codec which takes advantage of the following advances;
+NeuCodec is a Finite Scalar Quantisation (FSQ) based 0.8kbps audio codec for speech tokenization.
+It takes advantage of the following features:
 
-* It uses both audio (BigCodec encoder) and semantic (Wav2Vec2-BERT-large) information in the encoding process. 
-* We make use of Finite Scalar Quantisation (FSQ) resulting in a single vector for the quantised output, which makes it ideal for downstream modeling in SpeechLMs.
+* It uses both audio (BigCodec) and semantic ([Wav2Vec2-BERT](https://huggingface.co/facebook/w2v-bert-2.0)) encoders. 
+* We make use of Finite Scalar Quantisation (FSQ) resulting in a single vector for the quantised output, which makes it ideal for downstream modeling with Speech Language Models.
 * At 50 tokens/sec and 16 bits per token, the overall bit-rate is 0.8kbps.
+* The codec takes in 16kHz input and outputs 24kHz using an upsampling decoder.
 
 Our work largely based on extending the work of [X-Codec2.0](https://huggingface.co/HKUSTAudio/xcodec2).
 
 - **Developed by:** Neuphonic
 - **Model type:** Neural Audio Codec
-- **Language(s):** English
 - **License:** apache-2.0
-
-### Model Sources
-
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** https://github.com/neuphonic/neucodec
-- **Paper:** *Coming soon*
+- **Paper:** *Coming soon!*
 
 ## Get Started
 
@@ -82,44 +65,27 @@ with torch.no_grad():
     print(f"Codes shape: {fsq_codes.shape}")  
     recon = model.decode_code(fsq_codes).cpu() # (B, 1, T_24)
 
-sf.write("reconstructed.wav", recon_wav[0, 0, :].numpy(), sr)
+sf.write("reconstructed.wav", recon_wav[0, 0, :].numpy(), 24_000)
 ```
 
 ## Training Details
 
-The model was trained on a mix of publicly available and proprietary data. The publicly available data includes the English segments of Emilia-YODAS, MLS, LibriTTS, Fleurs, CommonVoice, and HUI. All publically available data was covered by either the CC-BY-4.0 or CC0 license.
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
+The model was trained using the following data: 
+* Emilia-YODAS
+* MLS
+* LibriTTS
+* Fleurs
+* CommonVoice
+* HUI
+* Additional proprietary set
 
-### Testing Data, Factors & Metrics
+All publically available data was covered by either the CC-BY-4.0 or CC0 license.
 
-#### Testing Data
 
-CMU-Arctic
-
-<!-- This should link to a Dataset Card if possible. -->
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-As we are interested the the degree of distortion from the unencoded to reconstructed audio, our evaluation metrics include. PESQ, STOI, SI-SDR, Mel-Spectrogram MSE, and diff WER.
-
-### Results
+### Benchmarks
 
 | Codec	| Quantizer Token Rate |	Tokens Per Second |	Bitrate |	Codebook size |	Quantizers |	Params |	Autoencoding RTF	| Decoding RTF |	WER (%) |	CER (%) |
 | -------- | ------- | -------- | ------- | -------- | ------- | -------- | ------- | -------- | ------- | -------- |
 | DAC 	|	75 |	600 |	6kbps |	1024 |	8 |	74.7 |	0.015 |	0.007 |	1.9 |	0.06 |
 | Mimi 	|	12.5 |	150	|1.1kbps |	2k	| 8| 	79.3| 	0.012|	0.006|	3.0|	1.4 |
 | NeuCodec |	50 |	50|	0.8kbps |	65k|	1|	800|	0.030|	0.003|	2.5|	1.0|
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-Coming Soon
-