Rename to CallEnhancer; remove train-time degradation details

README.md

@@ -18,7 +18,7 @@ base_model: facebook/w2v-bert-2.0
 pipeline_tag: audio-to-audio
 ---
 
-# Sidon — Call-Centre / Telephony Speech Restoration
+# CallEnhancer — Call-Centre / Telephony Speech Restoration
 
 Restore **narrowband, codec'd, noisy call-centre / telephony speech** (e.g. 8 kHz G.711/GSM phone
 audio) to **clean 48 kHz**. Two stages, both trained for the telephony domain (Malaysian/Singaporean
@@ -39,11 +39,11 @@ pip install torch torchaudio "transformers>=4.56" "descript-audio-codec>=1.0.0"
 
 # pull the CLI + the two slim checkpoints from the Hub
 hf auth login   # private repo: log in first (or export HF_TOKEN=hf_...)
-hf download Scicom-intl/sidon-callcentre \
+hf download Scicom-intl/CallEnhancer \
     infer_callcentre.py fe_callcentre/fe_adapter_full.pt \
-    decoder_callcentre/decoder_only.pt --local-dir sidon-callcentre
+    decoder_callcentre/decoder_only.pt --local-dir CallEnhancer
 
-cd sidon-callcentre && python infer_callcentre.py \
+cd CallEnhancer && python infer_callcentre.py \
     --input your_call.wav --out-dir out \
     --fe-adapter fe_callcentre/fe_adapter_full.pt \
     --decoder decoder_callcentre/decoder_only.pt \
@@ -53,8 +53,8 @@ cd sidon-callcentre && python infer_callcentre.py \
 
 Prefer Python (load weights from the Hub with `hf_hub_download`)? See **[Python](#python-pull-weights-from-the-hub)** below.
 
-> **Status:** the decoder is still training (~step 30k of 100k) and these checkpoints are refreshed
-> periodically — quality keeps improving. It already restores real 8 kHz call-centre audio well.
+> **Status:** the decoder is still training and these checkpoints are refreshed periodically —
+> quality keeps improving. It already restores real 8 kHz call-centre audio well.
 
 ## Files
 
@@ -77,12 +77,12 @@ pip install torch torchaudio "transformers>=4.56" "descript-audio-codec>=1.0.0"
 
 # pull the CLI + the two slim checkpoints, straight from this repo
 hf auth login   # private repo: log in first (or export HF_TOKEN=hf_...)
-hf download Scicom-intl/sidon-callcentre \
+hf download Scicom-intl/CallEnhancer \
     infer_callcentre.py \
     fe_callcentre/fe_adapter_full.pt \
     decoder_callcentre/decoder_only.pt \
-    --local-dir sidon-callcentre
-cd sidon-callcentre
+    --local-dir CallEnhancer
+cd CallEnhancer
 
 # restore your audio end-to-end
 python infer_callcentre.py \
@@ -114,7 +114,7 @@ from huggingface_hub import hf_hub_download
 from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
 import dac
 
-REPO, SSL, FE_SR, SR_OUT = "Scicom-intl/sidon-callcentre", "facebook/w2v-bert-2.0", 16000, 48000
+REPO, SSL, FE_SR, SR_OUT = "Scicom-intl/CallEnhancer", "facebook/w2v-bert-2.0", 16000, 48000
 dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 ck = torch.load(hf_hub_download(REPO, "fe_callcentre/fe_adapter_full.pt"), map_location="cpu")
@@ -150,8 +150,6 @@ restore("your_call.wav")   # <-- your own telephony/call-centre audio
   signal's features toward a frozen teacher on the *clean* signal (~16M trainable).
 - **Decoder:** `dac.model.dac.Decoder(input_channel=1024, channels=3072, rates=[8,5,4,3,2])` (188M,
   50 fps x 960 = 48 kHz), trained with DAC multi-resolution mel + GAN (`loss = 15*mel + 2*adv + 1*feat`).
-- **Degradation (train-time):** telephone HP -> narrowband ceiling (8/11/12/16k) -> GSM / G.711-mu-law
-  -> 16-40 kbps MP3 -> line noise + VoIP dropouts.
 - **Teachers (clean 48 kHz):** EARS + Expresso (studio) + DNSMOS-filtered multilingual HF datasets +
   DNSMOS-filtered Malaysian/Singaporean podcast & movie
   ([`Scicom-intl/sidon-callcentre-podcast`](https://huggingface.co/datasets/Scicom-intl/sidon-callcentre-podcast)).