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diff=lfs merge=lfs -text +ab_degrade_eb2/11_rainbow_03_fast_hard2.wav filter=lfs diff=lfs merge=lfs -text +ab_degrade_eb2/11_uk_ireland_en_008806_base.wav filter=lfs diff=lfs merge=lfs -text +ab_degrade_eb2/11_uk_ireland_en_008806_clean.wav filter=lfs diff=lfs merge=lfs -text +ab_degrade_eb2/11_uk_ireland_en_008806_hard1.wav filter=lfs diff=lfs merge=lfs -text +ab_degrade_eb2/11_uk_ireland_en_008806_hard2.wav filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..85eda2c9c82084fbbb8e5af13be2f72d8af766e9 --- /dev/null +++ b/README.md @@ -0,0 +1,142 @@ +--- +license: cc-by-nc-4.0 +tags: + - speech-restoration + - speech-enhancement + - telephony + - call-center + - w2v-bert + - lora + - sidon + - dac +base_model: facebook/w2v-bert-2.0 +pipeline_tag: audio-to-audio +--- + +# CallEnhancer — Call-Centre / Telephony Speech Restoration + +Restore Multi-lingual **narrowband, codec'd, noisy call-centre / telephony speech** (e.g. 8 kHz G.711/GSM phone +audio) to **clean 48 kHz**. + +## Benchmark — CER + +Character Error Rate (Whisper large-v3, **lower is better**) on a held-out **private call-centre +test set**. CallEnhancer restores telephony-degraded speech so an ASR reads it far more accurately +than the raw input or general-purpose speech enhancers. + +![CER on a private call-centre test set](assets/callcentre_cer.png) + +| model | CER % ↓ | +|---|---:| +| degraded (original) | 71.48 | +| voicefixer | 58.27 | +| resemble-enhance | 54.96 | +| sidon-v0.1 | 41.06 | +| **CallEnhancer-small** (this repo, open source) | **38.91** | +| **CallEnhancer-base** | **19.01** | + +This repository hosts **CallEnhancer-small** (open source); **CallEnhancer-base** is the larger model. + +## Quick start — infer from the HF checkpoint + +```bash +pip install torch torchaudio "transformers>=4.56" "descript-audio-codec>=1.0.0" soundfile "huggingface_hub[cli]" + +# 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/CallEnhancer \ + infer_callcentre.py fe_callcentre/fe_adapter_full.pt \ + decoder_callcentre/decoder_only.pt --local-dir CallEnhancer + +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 \ + --chunk 0 --device cuda # --chunk 0 = NO chunking (default single pass); --device cpu if no GPU +# -> out/your_call_restored48k.wav (clean 48 kHz) + out/your_call_orig48k.wav (A/B) +``` + +Prefer Python (load weights from the Hub with `hf_hub_download`)? See **[Python](#python-pull-weights-from-the-hub)** below. + +> **Status:** CallEnhancer-small is fully trained and restores real 8 kHz call-centre audio well. + +## Files + +Use the **current-run** checkpoints under `fe_callcentre/` and `decoder_callcentre/`: + +| path | role | size | +|---|---|---| +| `fe_callcentre/fe_adapter_full.pt` | **FE adapter (inference)** — 144 tensors: 96 LoRA + 48 trained `output_dense` biases | ~63 MB | +| `decoder_callcentre/decoder_only.pt` | **decoder (inference)** — 188M DAC decoder | ~0.75 GB | +| `fe_callcentre/last.pt`, `decoder_callcentre/last.pt` | raw checkpoints (resume training) | ~2.5 / 2.8 GB | +| `infer_callcentre.py` | inference CLI (below) | — | + +For inference you only need the two slim files + `infer_callcentre.py`. *(Root-level +`fe_adapter_full.pt` / `decoder_only.pt` are from an earlier run and are superseded.)* + +## End-to-end example (straight from HuggingFace) + +```bash +pip install torch torchaudio "transformers>=4.56" "descript-audio-codec>=1.0.0" soundfile "huggingface_hub[cli]" + +# 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/CallEnhancer \ + infer_callcentre.py \ + fe_callcentre/fe_adapter_full.pt \ + decoder_callcentre/decoder_only.pt \ + --local-dir CallEnhancer +cd CallEnhancer + +# restore your audio end-to-end +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 \ + --chunk 0 --device cuda # --chunk 0 = NO chunking (single straight pass, default); --device cpu if no GPU +``` + +Outputs: +- `out/your_call_restored48k.wav` — the restored **clean 48 kHz** speech. +- `out/your_call_orig48k.wav` — the input, naively upsampled to 48 kHz (no model), for an A/B listen. + +`--input` accepts a **file or a directory** (`.wav/.flac/.mp3/.ogg/.opus/.m4a`). Stereo (e.g. +agent/customer on separate channels) is restored per channel and recombined. + +### Python (pull weights from the Hub) + +```python +import numpy as np, soundfile as sf, torch, torchaudio +from huggingface_hub import hf_hub_download +from transformers import AutoFeatureExtractor, Wav2Vec2BertModel +import dac + +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") +ad, scale = ck["adapter"], ck["lora_alpha"] / ck["r"] +fe = Wav2Vec2BertModel.from_pretrained(SSL, num_hidden_layers=ck.get("layers", 24), layerdrop=0.0) +sd = fe.state_dict() # merge LoRA -> base (no peft needed) +for p in sorted({k[:-len(".lora_A.default.weight")] for k in ad if k.endswith(".lora_A.default.weight")}): + sd[p+".weight"] = sd[p+".weight"].float() + scale * (ad[p+".lora_B.default.weight"].float() @ ad[p+".lora_A.default.weight"].float()) + if p+".base_layer.bias" in ad: sd[p+".bias"] = ad[p+".base_layer.bias"].to(sd[p+".bias"].dtype) +fe.load_state_dict(sd); fe.to(dev).eval() + +dck = torch.load(hf_hub_download(REPO, "decoder_callcentre/decoder_only.pt"), map_location="cpu") +dec = dac.model.dac.Decoder(input_channel=1024, channels=dck.get("dec_channels", 3072), rates=[8,5,4,3,2]) +dec.load_state_dict(dck["decoder"]); dec.to(dev).eval() +proc = AutoFeatureExtractor.from_pretrained(SSL) + +@torch.no_grad() +def restore(path, out="restored48k.wav"): # single straight pass + x, sr = sf.read(path, dtype="float32"); x = x.mean(1) if x.ndim > 1 else x + if sr != FE_SR: x = torchaudio.functional.resample(torch.from_numpy(x)[None], sr, FE_SR)[0].numpy() + x = x / (np.abs(x).max() + 1e-9) * 0.95 + feats = {k: v.to(dev) for k, v in proc(x, sampling_rate=FE_SR, return_tensors="pt").items()} + y = dec(fe(**feats).last_hidden_state.transpose(1, 2)).squeeze().float().cpu().numpy() + sf.write(out, y / (np.abs(y).max() + 1e-9) * 0.97, SR_OUT); print("wrote", out) + +restore("your_call.wav") # <-- your own telephony/call-centre audio +``` diff --git a/assets/callcentre_cer.png b/assets/callcentre_cer.png new file mode 100644 index 0000000000000000000000000000000000000000..7a372ffee760ddb77785c554c38e8a9c4d436160 --- /dev/null +++ b/assets/callcentre_cer.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:857dc26bb4b97b90ad2d6d803e80db400e30b5e76ade82fcc301f3d7bf83c7af +size 100782 diff --git a/callhome_benchmark/callhome_benchmark.zip b/callhome_benchmark/callhome_benchmark.zip new file mode 100644 index 0000000000000000000000000000000000000000..9754187ccc5e205e0d2bca42b8a8e52936bd3770 --- /dev/null +++ b/callhome_benchmark/callhome_benchmark.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11b27646d8b9127f9ca5cfade46b38d70e289dac855c177ad1bce6c2764f1ad2 +size 10681510070 diff --git a/callhome_benchmark/results.md b/callhome_benchmark/results.md new file mode 100644 index 0000000000000000000000000000000000000000..5ee789225346026dcaff0c39c976e687c2d92ac3 --- /dev/null +++ b/callhome_benchmark/results.md @@ -0,0 +1,11 @@ +# CallHome restoration benchmark (UTMOS) + +talkbank/callhome, 30 s chunks, <=100/language. UTMOS via faster-UTMOSv2 (higher = better). No transcripts in this dataset, so no CER — perceptual quality only. Δ is vs the untouched **degraded** (original) audio. + +| model | UTMOS (↑) | Δ vs orig | deu | eng | jpn | spa | zho | n | +|---|---:|---:|---:|---:|---:|---:|---:|---:| +| degraded *(original)* | 1.538 | — | 1.541 | 1.814 | 1.545 | 1.221 | 1.570 | 500 | +| ours | 2.195 | +0.657 | 2.179 | 2.316 | 2.260 | 1.847 | 2.374 | 500 | +| sidon_v01 | 2.711 | +1.173 | 2.720 | 3.021 | 2.803 | 2.205 | 2.806 | 500 | +| voicefixer | 1.891 | +0.353 | 1.765 | 1.989 | 1.824 | 1.900 | 1.976 | 500 | +| resemble_enhance | 2.311 | +0.774 | 2.378 | 2.439 | 2.345 | 2.003 | 2.392 | 500 | diff --git a/decoder_callcentre/decoder_only.pt b/decoder_callcentre/decoder_only.pt new file mode 100644 index 0000000000000000000000000000000000000000..7e9e2e7275e9eccb1177875b5c4174f77623f200 --- /dev/null +++ b/decoder_callcentre/decoder_only.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5af53b93e918b279aa1a34179f164e0cecda6b532f2b338756b31c1e0d6e9c1 +size 750806394 diff --git 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to clean +48 kHz, using the two trained stages: + + input audio --resample 16k--> (call-centre FE: 24L w2v-BERT + LoRA) --features[T,1024]--> + (DAC decoder, 188M) --> 48 kHz waveform + +The FE LoRA adapter is **merged into the base weights** here (W_eff = W + (alpha/r)*B@A, +bias = trained bias), so inference needs NO `peft` — just transformers + descript-audio-codec. + +Usage: + python runpod/infer_callcentre.py --input audio --out-dir audio/out \ + --fe-adapter checkpoints/fe_adapter_full.pt --decoder checkpoints/decoder_only.pt + +Stereo inputs (e.g. agent/customer on separate channels) are restored per-channel +and recombined to stereo. A `_orig48k.wav` (naive-upsampled input, no model) +is also written for an apples-to-apples A/B listen. +""" +from __future__ import annotations + +import os + +os.environ.setdefault("OMP_NUM_THREADS", "4") + +import argparse +import glob +import time + +import numpy as np +import soundfile as sf +import torch +import torchaudio +from transformers import AutoFeatureExtractor, Wav2Vec2BertModel + +import dac + +SSL_MODEL = "facebook/w2v-bert-2.0" +FE_SR = 16000 +SR_OUT = 48000 +AUDIO_EXTS = (".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a") + + +def log(m: str) -> None: + print(m, flush=True) + + +def load_fe(adapter_path: str, device: torch.device) -> Wav2Vec2BertModel: + """Build the 24L w2v-BERT base and merge the trained LoRA adapter into it.""" + ck = torch.load(adapter_path, map_location="cpu") + ad = ck["adapter"] + scaling = ck["lora_alpha"] / ck["r"] + layers = ck.get("layers", 24) + model = Wav2Vec2BertModel.from_pretrained(SSL_MODEL, num_hidden_layers=layers, layerdrop=0.0) + sd = model.state_dict() + prefixes = sorted({k[: -len(".lora_A.default.weight")] + for k in ad if k.endswith(".lora_A.default.weight")}) + merged = 0 + for p in prefixes: # p e.g. encoder.layers.0.ffn1.output_dense + A = ad[p + ".lora_A.default.weight"].float() # (r, in) + B = ad[p + ".lora_B.default.weight"].float() # (out, r) + delta = scaling * (B @ A) # (out, in) + wkey = p + ".weight" + sd[wkey] = sd[wkey].float() + delta.to(sd[wkey].dtype) + bkey = p + ".base_layer.bias" # trained (lora_only) bias + if bkey in ad: + sd[p + ".bias"] = ad[bkey].to(sd[p + ".bias"].dtype) + merged += 1 + model.load_state_dict(sd) + model.to(device).eval() + for q in model.parameters(): + q.requires_grad_(False) + log(f"[fe] merged LoRA into {merged} output_dense layers (scaling={scaling}); " + f"step {ck.get('step')}") + return model + + +def load_decoder(path: str, device: torch.device): + ck = torch.load(path, map_location="cpu") + ch = ck.get("dec_channels", 3072) + dec = dac.model.dac.Decoder(input_channel=1024, channels=ch, rates=[8, 5, 4, 3, 2]) + dec.load_state_dict(ck["decoder"]) + dec.to(device).eval() + for q in dec.parameters(): + q.requires_grad_(False) + log(f"[dec] DAC decoder channels={ch} ({sum(p.numel() for p in dec.parameters())/1e6:.1f}M); " + f"step {ck.get('step')}") + return dec + + +def _peak_norm(x: np.ndarray, peak: float = 0.95) -> np.ndarray: + m = float(np.abs(x).max()) + return (x / m * peak).astype("float32") if m > 1e-6 else x.astype("float32") + + +@torch.no_grad() +def restore_channel(wav16: np.ndarray, fe, dec, proc, device, chunk_s: float, bf16: bool): + """wav16: 1-D float32 @16k (peak-normalized) -> restored 1-D float32 @48k. + chunk_s <= 0 => single straight pass (recommended: w2v-BERT uses relative/rotary + position embeddings and the DAC decoder is convolutional, so a full pass is + length-invariant and cleaner). Windowing (chunk_s > 0) is only a memory fallback + for very long audio (self-attention is O(T^2)); it crossfades outputs to avoid clicks.""" + n = len(wav16) + win = int(chunk_s * FE_SR) + if chunk_s <= 0 or n <= win: + bounds = [(0, n)] + else: + ov = int(2.0 * FE_SR) # 2 s overlap + hop = win - ov + bounds = [(s, min(s + win, n)) for s in range(0, n, hop)] + bounds = [b for b in bounds if b[1] > b[0]] + out = np.zeros(n * 3 + SR_OUT, dtype="float32") # 16k->48k is x3; pad slack + wsum = np.zeros_like(out) + amp = torch.bfloat16 if (bf16 and device.type == "cuda") else torch.float32 + for s, e in bounds: + seg = np.pad(wav16[s:e], (40, 40)) + feats_in = proc(seg, sampling_rate=FE_SR, return_tensors="pt") + feats_in = {k: v.to(device) for k, v in feats_in.items()} + with torch.autocast(device.type, dtype=amp, enabled=(amp == torch.bfloat16)): + h = fe(**feats_in).last_hidden_state # [1,T,1024] + y = dec(h.transpose(1, 2)) # [1,1,L] @48k + y = y.squeeze().float().cpu().numpy() + o0 = s * 3 + L = min(len(y), len(out) - o0) + ramp = np.ones(L, dtype="float32") + if len(bounds) > 1: # taper edges for crossfade + r = min(int(2.0 * SR_OUT), L // 2) + if r > 0: + ramp[:r] = np.linspace(0, 1, r) + ramp[-r:] = np.linspace(1, 0, r) + out[o0:o0 + L] += y[:L] * ramp + wsum[o0:o0 + L] += ramp + valid = wsum > 1e-6 + out[valid] /= wsum[valid] + return out[: n * 3] + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--input", default="audio", help="audio file or directory") + ap.add_argument("--out-dir", default="audio/out") + ap.add_argument("--fe-adapter", default="checkpoints/fe_adapter_full.pt") + ap.add_argument("--decoder", default="checkpoints/decoder_only.pt") + ap.add_argument("--chunk", type=float, default=0.0, + help="0 = single straight pass (default, recommended). >0 = window seconds " + "(memory fallback for very long audio; crossfaded).") + ap.add_argument("--mono", action="store_true", help="downmix to mono instead of per-channel") + ap.add_argument("--bf16", type=int, default=1) + ap.add_argument("--device", default="cuda") + a = ap.parse_args() + dev = torch.device(a.device if torch.cuda.is_available() else "cpu") + torch.set_float32_matmul_precision("medium") + os.makedirs(a.out_dir, exist_ok=True) + + if os.path.isdir(a.input): + files = sorted(f for f in glob.glob(os.path.join(a.input, "*")) + if f.lower().endswith(AUDIO_EXTS)) + else: + files = [a.input] + if not files: + raise SystemExit(f"no audio under {a.input}") + + fe = load_fe(a.fe_adapter, dev) + dec = load_decoder(a.decoder, dev) + proc = AutoFeatureExtractor.from_pretrained(SSL_MODEL) + + for path in files: + name = os.path.splitext(os.path.basename(path))[0] + data, sr = sf.read(path, always_2d=True, dtype="float32") # [N, C] + if a.mono: + data = data.mean(axis=1, keepdims=True) + nch = data.shape[1] + t0 = time.time() + chans = [] + for c in range(nch): + x = data[:, c] + x16 = (torchaudio.functional.resample(torch.from_numpy(x)[None], sr, FE_SR)[0].numpy() + if sr != FE_SR else x) + x16 = _peak_norm(x16, 0.95) + y48 = restore_channel(x16, fe, dec, proc, dev, a.chunk, bool(a.bf16)) + chans.append(_peak_norm(y48, 0.97)) + L = max(len(c) for c in chans) + chans = [np.pad(c, (0, L - len(c))) for c in chans] + restored = np.stack(chans, axis=1) # [L, C] + + # naive-upsampled input (no model) for an A/B reference + orig = torchaudio.functional.resample(torch.from_numpy(data.T), sr, SR_OUT).T.numpy() + sf.write(os.path.join(a.out_dir, f"{name}_orig48k.wav"), _peak_norm(orig, 0.97), SR_OUT) + sf.write(os.path.join(a.out_dir, f"{name}_restored48k.wav"), restored, SR_OUT) + dur = data.shape[0] / sr + log(f"[ok] {name}: {nch}ch {sr}Hz {dur:.1f}s -> 48k in {time.time()-t0:.1f}s " + f"(RTF {(time.time()-t0)/dur:.2f})") + + log(f"[done] outputs in {a.out_dir}") + + +if __name__ == "__main__": + main()