Update README.md

README.md

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----
-license: mit
-tags:
-  - audio
-  - music-source-separation
-  - sound-separation
-  - demucs
-  - htdemucs
-  - stem-separation
-  - inference
-pipeline_tag: audio-to-audio
----
-
-## Music Source Separation
-
-This is the Demucs model, serialized from Facebook Research's pretrained models. 
-
----
-
-## What is HTDemucs?
-
-[HTDemucs (Hybrid Transformer Demucs)](https://github.com/facebookresearch/demucs) is Meta AI's fourth-generation music source separation model, introduced in [*Hybrid Transformers for Music Source Separation* (Rouard et al., ICASSP 2023)](https://arxiv.org/abs/2211.08553).
-
-Where earlier Demucs generations processed audio purely in the time domain, HTDemucs runs **two parallel encoders simultaneously** — one operating on the raw waveform, the other on the STFT spectrogram — with a **Transformer Encoder with cross-attention** at the bottleneck connecting them. This lets the model correlate time-domain and frequency-domain features before decoding, yielding measurably better separation quality — especially on spectrally complex, temporally sparse instruments like piano and guitar.
-
-The `htdemucs_6s` variant adds dedicated guitar and piano stems on top of the standard drums/bass/other/vocals quad, making it the most capable publicly available separation model for music production use.
-
----
-
-From Facebook research:
-
-    Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights and transposed convolutions in the decoder.
-
+---
+license: mit
+tags:
+  - audio
+  - music-source-separation
+  - sound-separation
+  - demucs
+  - htdemucs
+  - stem-separation
+  - inference
+pipeline_tag: audio-to-audio
+---
+
+## Music Source Separation
+
+This is the Demucs v4 models from Facebook Research. 
+
+---
+
+## What is HTDemucs?
+
+[HTDemucs (Hybrid Transformer Demucs)](https://github.com/facebookresearch/demucs) is Meta AI's fourth-generation music source separation model, introduced in [*Hybrid Transformers for Music Source Separation* (Rouard et al., ICASSP 2023)](https://arxiv.org/abs/2211.08553).
+
+Where earlier Demucs generations processed audio purely in the time domain, HTDemucs runs **two parallel encoders simultaneously** — one operating on the raw waveform, the other on the STFT spectrogram — with a **Transformer Encoder with cross-attention** at the bottleneck connecting them. This lets the model correlate time-domain and frequency-domain features before decoding, yielding measurably better separation quality — especially on spectrally complex, temporally sparse instruments like piano and guitar.
+
+The `htdemucs_6s` variant adds dedicated guitar and piano stems on top of the standard drums/bass/other/vocals quad, making it the most capable publicly available separation model for music production use.
+
+---
+
+From Facebook research:
+
+    Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights and transposed convolutions in the decoder.
+
 See [facebookresearch's repository](https://github.com/facebookresearch/demucs) for more information on Demucs.