Update README.md

README.md

@@ -1,115 +1,115 @@
----
-license: mit
-library_name: pytorch
-tags:
-  - audio
-  - spoofing-detection
-  - anti-spoofing
-  - wav2vec2
-  - ecapa-tdnn
----
-
-## Model Card: Spectra-0 (anti-spoofing / bonafide vs spoof)
-
-`Spectra-0` is a model for **speech spoofing detection** (binary classification: `bonafide` vs `spoof`) from **raw audio waveforms**. Architecture: SSL encoder (`Wav2Vec2`) → MLP projection → `ECAPA-TDNN` 2-class classifier.
-
-- **Input**: waveform \(float32\), shape `(batch, num_samples)` (typically 16 kHz).
-- **Output**: logits of shape `(batch, 2)`, where **index 0 = spoof**, **index 1 = bonafide**.
-
-On first run, the model will automatically download the SSL encoder `facebook/wav2vec2-xls-r-300m` via `transformers`.
-
-## Quickstart
-
-### Clone from Hugging Face
-
-This repository is hosted on Hugging Face Hub: `https://huggingface.co/MTUCI/spectra_0`.
-
-```bash
-git lfs install
-git clone https://huggingface.co/MTUCI/spectra_0
-cd spectra_0
-```
-
-### Install dependencies
-
-```bash
-pip install -U torch torchaudio transformers huggingface_hub safetensors soundfile
-```
-
-### Single-file inference (example preprocessing)
-
-```python
-import random
-import torch
-import torchaudio
-import soundfile as sf
-
-from model import spectra_0
-
-
-def pad_random(x: torch.Tensor, max_len: int = 64600) -> torch.Tensor:
-    # x: (num_samples,) or (1, num_samples)
-    if x.ndim > 1:
-        x = x.squeeze()
-    x_len = x.shape[0]
-    if x_len >= max_len:
-        start = random.randint(0, x_len - max_len)
-        return x[start:start + max_len]
-    num_repeats = int(max_len / x_len) + 1
-    return x.repeat(num_repeats)[:max_len]
-
-
-def load_audio_mono(path: str) -> torch.Tensor:
-    audio, sr = sf.read(path, dtype="float32")
-    audio = torch.from_numpy(audio)
-    if audio.ndim > 1:
-        # (num_samples, channels) -> mono
-        audio = audio.mean(dim=1)
-    if sr != 16000:
-        audio = torchaudio.functional.resample(audio, sr, 16000)
-    return audio
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = spectra_0.from_pretrained(pretrained_model_name_or_path=".").eval().to(device)
-
-audio = load_audio_mono("path/to/audio.wav")
-audio = torchaudio.functional.preemphasis(audio.unsqueeze(0))  # (1, T)
-audio = pad_random(audio.squeeze(0), 64600).unsqueeze(0)       # (1, 64600)
-
-with torch.inference_mode():
-    logits = model(audio.to(device))  # (1, 2)
-    score_spoof = logits[0, 0].item()
-    score_bonafide = logits[0, 1].item()
-
-print({"score_bonafide": score_bonafide, "score_spoof": score_spoof})
-```
-
-## Threshold-based classification (and how to tune it)
-
-In `model.py`, the `Spectra0Model` class provides `classify()` with a **default threshold** chosen as an “optimal” value for the original setting:
-
-- **Default threshold**: `-1.0625009` (it thresholds `logit_bonafide = logits[:, 1]`)
-- **Note**: this threshold **may not be optimal** on a different dataset/domain. It’s recommended to tune the threshold on your dataset using **EER** (Equal Error Rate) or a target FAR/FRR.
-
-Example:
-
-```python
-with torch.inference_mode():
-    pred = model.classify(audio.to(device), threshold=-1.0625009)  # 1=bonafide, 0=spoof
-```
-
-### Tuning the threshold via EER (typical workflow)
-
-1) Run the model on a labeled set and collect scores for both classes (e.g., store `score_bonafide = logits[:, 1]` for each sample).
-
-2) Compute EER and the threshold
-
-## Limitations and notes
-
-- This is a **pre-release** model.
-- Significantly stronger models are planned for **Q3–Q4 2026** — stay tuned.
-
-## License
-
-MIT (see the `license` field in the model repo header).
+---
+license: mit
+library_name: pytorch
+tags:
+  - audio
+  - spoofing-detection
+  - anti-spoofing
+  - wav2vec2
+  - ecapa-tdnn
+---
+
+## Model Card: Spectra-0 (anti-spoofing / bonafide vs spoof)
+
+`Spectra-0` is a model for **speech spoofing detection** (binary classification: `bonafide` vs `spoof`) from **raw audio waveforms**. Architecture: SSL encoder (`Wav2Vec2`) → MLP projection → `ECAPA-TDNN` 2-class classifier.
+
+- **Input**: waveform \(float32\), shape `(batch, num_samples)` (typically 16 kHz).
+- **Output**: logits of shape `(batch, 2)`, where **index 0 = spoof**, **index 1 = bonafide**.
+
+On first run, the model will automatically download the SSL encoder `facebook/wav2vec2-xls-r-300m` via `transformers`.
+
+## Quickstart
+
+### Clone from Hugging Face
+
+This repository is hosted on Hugging Face Hub: `https://huggingface.co/MTUCI/spectra_0`.
+
+```bash
+git lfs install
+git clone https://huggingface.co/MTUCI/spectra_0
+cd spectra_0
+```
+
+### Install dependencies
+
+```bash
+pip install -U torch torchaudio transformers huggingface_hub safetensors soundfile
+```
+
+### Single-file inference (example preprocessing)
+
+```python
+import random
+import torch
+import torchaudio
+import soundfile as sf
+
+from model import spectra_0
+
+
+def pad_random(x: torch.Tensor, max_len: int = 64600) -> torch.Tensor:
+    # x: (num_samples,) or (1, num_samples)
+    if x.ndim > 1:
+        x = x.squeeze()
+    x_len = x.shape[0]
+    if x_len >= max_len:
+        start = random.randint(0, x_len - max_len)
+        return x[start:start + max_len]
+    num_repeats = int(max_len / x_len) + 1
+    return x.repeat(num_repeats)[:max_len]
+
+
+def load_audio_mono(path: str) -> torch.Tensor:
+    audio, sr = sf.read(path, dtype="float32")
+    audio = torch.from_numpy(audio)
+    if audio.ndim > 1:
+        # (num_samples, channels) -> mono
+        audio = audio.mean(dim=1)
+    if sr != 16000:
+        audio = torchaudio.functional.resample(audio, sr, 16000)
+    return audio
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = spectra_0.from_pretrained(pretrained_model_name_or_path=".").eval().to(device)
+
+audio = load_audio_mono("path/to/audio.wav")
+audio = torchaudio.functional.preemphasis(audio.unsqueeze(0))  # (1, T)
+audio = pad_random(audio.squeeze(0), 64600).unsqueeze(0)       # (1, 64600)
+
+with torch.inference_mode():
+    logits = model(audio.to(device))  # (1, 2)
+    score_spoof = logits[0, 0].item()
+    score_bonafide = logits[0, 1].item()
+
+print({"score_bonafide": score_bonafide, "score_spoof": score_spoof})
+```
+
+## Threshold-based classification (and how to tune it)
+
+In `model.py`, the `Spectra0Model` class provides `classify()` with a **default threshold** chosen as an “optimal” value for the original setting:
+
+- **Default threshold**: `-1.0625009` (it thresholds `logit_bonafide = logits[:, 1]`)
+- **Note**: this threshold **may not be optimal** on a different dataset/domain. It’s recommended to tune the threshold on your dataset using **EER** (Equal Error Rate) or a target FAR/FRR.
+
+Example:
+
+```python
+with torch.inference_mode():
+    pred = model.classify(audio.to(device), threshold=-1.0625009)  # 1=bonafide, 0=spoof
+```
+
+### Tuning the threshold via EER (typical workflow)
+
+1) Run the model on a labeled set and collect scores for both classes.
+
+2) Compute EER and the threshold
+
+## Limitations and notes
+
+- This is a **pre-release** model.
+- Significantly stronger models are planned for **Q3–Q4 2026** — stay tuned.
+
+## License
+
+MIT (see the `license` field in the model repo header).