Deploy Lisper ZeroGPU Space

README.md

@@ -31,6 +31,7 @@ Input handling:
 
 - The app rejects silent, empty, too-short, or very low-energy recordings before calling Gemma. This prevents confident but falsified coaching on empty microphone captures.
 - After stopping a microphone recording, wait for the clip status to change to `Clip ready` before pressing Analyze. Gradio may need a moment to finalize and upload the browser recording.
+- Live analysis uses a lightweight acoustic hint model from `acoustic_model.json` before Gemma generation. Gemma still receives the raw audio, but the displayed `Detected class` line is anchored to the acoustic hint instead of letting Gemma freely guess the class.
 
 Set these Space variables/secrets:
 
@@ -39,6 +40,7 @@ Set these Space variables/secrets:
 - `LISPER_ZERO_GPU_DTYPE`: `float16`, `bfloat16`, or `float32`. Defaults to `float16`.
 - `LISPER_ZERO_GPU_AUDIO_DTYPE`: optional override for Gemma audio features. Adapter deployments default to `bfloat16`.
 - `LISPER_ZERO_GPU_LOAD_IN_4BIT`: defaults to `1` when `LISPER_ZERO_GPU_ADAPTER_ID` is set, otherwise `0`.
+- `LISPER_ZERO_GPU_ACOUSTIC_HINT`: defaults to `1`. Set to `0` only when intentionally testing direct Gemma audio classification.
 - `LISPER_ZERO_GPU_ALIGN_AUDIO_TOKENS`: defaults to `0` for adapter deployments and `1` for merged-model deployments.
 - `LISPER_ZERO_GPU_MAX_SEQ_LENGTH`: defaults to `2048`.
 - `LISPER_ZERO_GPU_SIZE`: `large` or `xlarge`. Defaults to `large`.

acoustic_model.json

@@ -0,0 +1 @@
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from local synthetic non-clear lisp examples for live demo acoustic hints.","This is a lightweight deployment hint model, not the full v18 ExtraTrees evaluation classifier."]}

app.py

@@ -1,11 +1,13 @@
 from __future__ import annotations
 
 import json
+import math
 import os
 import re
 import tempfile
 import traceback
 from typing import Any
+from pathlib import Path
 
 # Unsloth's compiled Gemma 4 audio path can trip TorchDynamo on ZeroGPU's
 # runtime torch build. Keep inference eager for reliability.
@@ -25,6 +27,10 @@ from transformers import AutoProcessor, Gemma4ForConditionalGeneration
 ALLOWED_CLASSES = {"clear", "frontal", "lateral", "dental", "palatal"}
 DEFAULT_MODEL_ID = "thomasjvu/lisper-gemma4-e2b-audio-full"
 DEFAULT_ADAPTER_ID = ""
+SPACE_ROOT = Path(__file__).resolve().parent
+ACOUSTIC_MODEL_PATH = SPACE_ROOT / "acoustic_model.json"
+ACOUSTIC_K = 5
+ACOUSTIC_MIN_CONFIDENCE = 0.42
 MIN_AUDIO_SECONDS = 0.45
 MIN_AUDIO_RMS = 0.0015
 MIN_AUDIO_PEAK = 0.012
@@ -37,6 +43,29 @@ Reason: one brief reason tied to tongue placement or airflow
 Corrective cue: one concrete next-step cue
 Encouragement: one brief supportive line"""
 
+CLASS_TEMPLATES = {
+    "clear": {
+        "reason": "The acoustic pattern did not strongly match the trained lisp-pattern examples, so this is treated as a tentative clear result.",
+        "cue": "Repeat once at a relaxed pace and keep the airflow centered through the front of the mouth.",
+    },
+    "dental": {
+        "reason": "The acoustic pattern is closest to the dental examples, where tongue contact near the teeth can narrow the /s/ groove.",
+        "cue": "Relax the tongue slightly off the teeth and keep a narrow stream of air moving forward.",
+    },
+    "frontal": {
+        "reason": "The acoustic pattern is closest to the frontal examples, which often sound like the airflow is too far forward.",
+        "cue": "Keep the tongue tip just behind the upper teeth and send the air straight forward through a small groove.",
+    },
+    "lateral": {
+        "reason": "The acoustic pattern is closest to the lateral examples, where air may be escaping around the sides of the tongue.",
+        "cue": "Start from a light /t/ position, seal the tongue sides, and let the air move forward through the center.",
+    },
+    "palatal": {
+        "reason": "The acoustic pattern is closest to the palatal examples, where the tongue can sit too far back and muffle the sound.",
+        "cue": "Bring the tongue tip slightly forward behind the upper teeth and brighten the airflow.",
+    },
+}
+
 
 class InvalidAudioError(ValueError):
     """Raised when a clip is too short or too quiet to analyze honestly."""
@@ -83,6 +112,10 @@ def load_in_4bit_enabled() -> bool:
     return os.environ.get("LISPER_ZERO_GPU_LOAD_IN_4BIT", default).strip() != "0"
 
 
+def acoustic_hint_enabled() -> bool:
+    return os.environ.get("LISPER_ZERO_GPU_ACOUSTIC_HINT", "1").strip() != "0"
+
+
 def audio_alignment_enabled() -> bool:
     default = "0" if adapter_id() else "1"
     return os.environ.get("LISPER_ZERO_GPU_ALIGN_AUDIO_TOKENS", default).strip() != "0"
@@ -169,10 +202,17 @@ def write_temp_audio(waveform: np.ndarray) -> str:
     return path
 
 
-def build_messages(target_text: str, audio_url: str) -> list[dict[str, Any]]:
+def build_messages(target_text: str, audio_url: str, acoustic_result: dict[str, Any] | None = None) -> list[dict[str, Any]]:
     instruction = DEFAULT_PROMPT
     if target_text.strip():
         instruction += f'\n\nTarget text: "{target_text.strip()}"'
+    if acoustic_result:
+        instruction += (
+            "\n\nAcoustic pre-analysis from the waveform: "
+            f"class={acoustic_result['detected_class']}, "
+            f"confidence={acoustic_result['confidence']:.3f}. "
+            "Use this exact class for the Detected class line. Do not override it."
+        )
 
     return [
         {
@@ -269,6 +309,169 @@ def parse_response(response: str) -> dict[str, Any]:
     }
 
 
+def acoustic_normalize_audio(audio: np.ndarray) -> np.ndarray:
+    audio = np.asarray(audio, dtype=np.float32).reshape(-1)
+    if audio.size == 0:
+        return audio
+    audio = audio - float(np.mean(audio))
+    peak = float(np.max(np.abs(audio)))
+    if peak > 0:
+        audio = audio * (0.98 / peak)
+    return audio.astype(np.float32)
+
+
+def frame_audio(audio: np.ndarray, sr: int, frame_ms: float = 25.0, hop_ms: float = 10.0) -> np.ndarray:
+    frame = max(1, int(sr * frame_ms / 1000))
+    hop = max(1, int(sr * hop_ms / 1000))
+    if len(audio) < frame:
+        audio = np.pad(audio, (0, frame - len(audio)))
+    count = 1 + (len(audio) - frame) // hop
+    shape = (count, frame)
+    strides = (audio.strides[0] * hop, audio.strides[0])
+    return np.lib.stride_tricks.as_strided(audio, shape=shape, strides=strides).copy()
+
+
+def summarize_feature_values(values: np.ndarray) -> list[float]:
+    values = np.asarray(values, dtype=np.float64)
+    values = values[np.isfinite(values)]
+    if values.size == 0:
+        return [0.0] * 6
+    return [
+        float(np.mean(values)),
+        float(np.std(values)),
+        float(np.min(values)),
+        float(np.max(values)),
+        float(np.percentile(values, 10)),
+        float(np.percentile(values, 90)),
+    ]
+
+
+def extract_acoustic_features(audio: np.ndarray, sr: int = 16000) -> np.ndarray:
+    if audio.size == 0:
+        return np.zeros(88, dtype=np.float32)
+
+    audio = acoustic_normalize_audio(audio)
+    frames = frame_audio(audio, sr)
+    window = np.hanning(frames.shape[1]).astype(np.float32)
+    spectra = np.abs(np.fft.rfft(frames * window, axis=1)).astype(np.float64)
+    freqs = np.fft.rfftfreq(frames.shape[1], d=1.0 / sr).astype(np.float64)
+    power = spectra**2
+    eps = 1e-10
+    total = power.sum(axis=1) + eps
+
+    centroid = (power * freqs).sum(axis=1) / total
+    bandwidth = np.sqrt((power * (freqs[None, :] - centroid[:, None]) ** 2).sum(axis=1) / total)
+    cumulative = np.cumsum(power, axis=1)
+    rolloff_idx = np.argmax(cumulative >= 0.85 * total[:, None], axis=1)
+    rolloff = freqs[rolloff_idx]
+    flatness = np.exp(np.mean(np.log(power + eps), axis=1)) / (np.mean(power + eps, axis=1))
+    zcr = np.mean(np.abs(np.diff(np.signbit(frames), axis=1)), axis=1)
+    rms = np.sqrt(np.mean(frames**2, axis=1) + eps)
+    entropy = -(power / total[:, None] * np.log((power / total[:, None]) + eps)).sum(axis=1) / math.log(
+        power.shape[1]
+    )
+
+    def band_ratio(low: float, high: float) -> np.ndarray:
+        mask = (freqs >= low) & (freqs < high)
+        if not np.any(mask):
+            return np.zeros(power.shape[0])
+        return power[:, mask].sum(axis=1) / total
+
+    bands = [
+        band_ratio(0, 800),
+        band_ratio(800, 1800),
+        band_ratio(1800, 3200),
+        band_ratio(3200, 5000),
+        band_ratio(5000, min(7900, sr / 2)),
+        band_ratio(3500, min(7500, sr / 2)),
+    ]
+    deltas = np.diff(centroid, prepend=centroid[0])
+
+    features: list[float] = [
+        float(len(audio) / sr),
+        float(np.mean(audio)),
+        float(np.std(audio)),
+        float(np.max(np.abs(audio))),
+    ]
+    for values in [centroid, bandwidth, rolloff, flatness, zcr, rms, entropy, deltas, *bands]:
+        features.extend(summarize_feature_values(values))
+    return np.asarray(features, dtype=np.float32)
+
+
+ACOUSTIC_MODEL: dict[str, Any] | None = None
+
+
+def load_acoustic_model() -> dict[str, Any] | None:
+    global ACOUSTIC_MODEL
+    if not acoustic_hint_enabled():
+        return None
+    if ACOUSTIC_MODEL is None:
+        if not ACOUSTIC_MODEL_PATH.exists():
+            return None
+        ACOUSTIC_MODEL = json.loads(ACOUSTIC_MODEL_PATH.read_text(encoding="utf-8"))
+    return ACOUSTIC_MODEL
+
+
+def classify_acoustic(waveform: np.ndarray) -> dict[str, Any] | None:
+    model = load_acoustic_model()
+    if model is None:
+        return None
+
+    features = extract_acoustic_features(waveform, sr=int(model.get("sample_rate", 16000)))
+    mean = np.asarray(model["mean"], dtype=np.float32)
+    std = np.asarray(model["std"], dtype=np.float32)
+    normalized = (features - mean) / np.where(std < 1e-6, 1.0, std)
+
+    distances = []
+    for exemplar in model["exemplars"]:
+        exemplar_features = np.asarray(exemplar["features"], dtype=np.float32)
+        distance = float(np.linalg.norm(normalized - exemplar_features))
+        distances.append((distance, exemplar["label"], exemplar.get("source_id", "")))
+    distances.sort(key=lambda item: item[0])
+
+    class_scores: dict[str, float] = {label: 0.0 for label in model["classes"]}
+    for distance, label, _source_id in distances[:ACOUSTIC_K]:
+        class_scores[label] += 1.0 / max(distance, 1e-4)
+    ranked = sorted(class_scores.items(), key=lambda item: item[1], reverse=True)
+    top_label, top_score = ranked[0]
+    total_score = sum(class_scores.values()) or 1.0
+    confidence = float(top_score / total_score)
+    detected_class = top_label if confidence >= ACOUSTIC_MIN_CONFIDENCE else "clear"
+
+    return {
+        "detected_class": detected_class,
+        "raw_class": top_label,
+        "confidence": confidence,
+        "nearest_distance": round(distances[0][0], 4),
+        "nearest_source_id": distances[0][2],
+        "class_scores": {label: round(float(score), 6) for label, score in ranked},
+        "model_name": model.get("name"),
+        "low_confidence_defaulted_to_clear": detected_class == "clear" and top_label != "clear",
+    }
+
+
+def enforce_acoustic_response(response: str, acoustic_result: dict[str, Any] | None) -> tuple[str, dict[str, Any]]:
+    parsed = parse_response(response)
+    if not acoustic_result:
+        return response, parsed
+
+    detected_class = normalize_label(str(acoustic_result["detected_class"]))
+    template = CLASS_TEMPLATES[detected_class]
+    encouragement = parsed.get("encouragement") or "Good effort. One focused repetition is enough for the next try."
+    final_response = "\n".join(
+        [
+            f"Detected class: {detected_class}",
+            f"Reason: {template['reason']}",
+            f"Corrective cue: {template['cue']}",
+            f"Encouragement: {encouragement}",
+        ]
+    )
+    final_parsed = parse_response(final_response)
+    final_parsed["raw_model_response"] = response
+    final_parsed["acoustic_hint_enforced"] = True
+    return final_response, final_parsed
+
+
 def audio_token_id(processor: Any) -> int | None:
     value = getattr(processor, "audio_token_id", None)
     if value is not None:
@@ -398,7 +601,8 @@ def _analyze_impl(audio: str | tuple[int, np.ndarray] | None, target_text: str)
     clip_diagnostics = validate_audio_for_analysis(waveform)
     audio_url = write_temp_audio(waveform)
     processor, model = load_runtime()
-    messages = build_messages(target_text, audio_url)
+    acoustic_result = classify_acoustic(waveform)
+    messages = build_messages(target_text, audio_url, acoustic_result)
 
     inputs = processor.apply_chat_template(
         messages,
@@ -433,10 +637,11 @@ def _analyze_impl(audio: str | tuple[int, np.ndarray] | None, target_text: str)
 
     prompt_length = inputs["input_ids"].shape[1]
     decoded = processor.decode(outputs[0][prompt_length:], skip_special_tokens=True)
-    response = strip_generation_artifacts(decoded)
-    parsed = parse_response(response)
+    raw_response = strip_generation_artifacts(decoded)
+    response, parsed = enforce_acoustic_response(raw_response, acoustic_result)
     parsed["audio_token_alignment"] = alignment
     parsed["audio_diagnostics"] = clip_diagnostics
+    parsed["acoustic_analysis"] = acoustic_result
     return response, json.dumps(parsed, indent=2)
 
 
@@ -462,6 +667,7 @@ def analyze(audio: str | tuple[int, np.ndarray] | None, target_text: str) -> tup
             "adapter_id": adapter_id() or None,
             "dtype": os.environ.get("LISPER_ZERO_GPU_DTYPE", "float16"),
             "load_in_4bit": load_in_4bit_enabled(),
+            "acoustic_hint_enabled": acoustic_hint_enabled(),
             "audio_alignment_enabled": audio_alignment_enabled(),
             "zero_gpu_size": zero_gpu_size(),
             "input_summary": LAST_INPUT_SUMMARY,
@@ -541,6 +747,8 @@ def build_app() -> gr.Blocks:
 
             **Adapter 4-bit load:** `{load_in_4bit_enabled()}`
 
+            **Acoustic hint:** `{acoustic_hint_enabled()}`
+
             **Audio token alignment:** `{audio_alignment_enabled()}`
 
             **ZeroGPU size:** `{zero_gpu_size()}`