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What is audiotimm?

audiotimm is a standalone Python library that lets you classify, tag, detect events in, and extract embeddings from audio — in one line — using state-of-the-art pretrained models. It is designed after the philosophy of timm: a unified registry where every model family (PANNs, AST, BEATs, HTS-AT, CLAP, Wav2Vec2, WavLM, Whisper, …) is accessible through a single, stable API.

from audiotimm import Classifier

clf = Classifier.load()                    # default: panns-cnn14
result = clf.predict("dog.wav")

result.top(5)       # [(label, score), ...]
result.label        # "Dog"
result.scores       # {"Dog": 0.94, "Animal": 0.72, ...}

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Highlights

One line to classify	Classifier.load().predict("x.wav").top(3) — weights download and cache automatically
Every major architecture	PANNs, YAMNet, AST, BEATs, HTS-AT, AudioMAE, CLAP, Wav2Vec2, HuBERT, WavLM, Whisper
Lean core	Zero heavy deps at import time — torch + torchaudio only for the default model
Rich result object	.top(k), .above(thresh), .label, .scores, .as_dict(), .embed()
Extensible	@register_model decorator to plug in custom architectures
CLI included	audiotimm predict dog.wav --top 5

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Installation

# Core (PANNs CNN-family, Wave M0)
pip install audiotimm

# + Transformer taggers: AST, BEATs, HTS-AT, AudioMAE (Wave M1)
pip install audiotimm[transformers]

# + Zero-shot classification via CLAP (Wave M2)
pip install audiotimm[clap]

# + Speech SSL backbones: Wav2Vec2, HuBERT, WavLM (Wave M3)
pip install audiotimm[speech]

# + Whisper ASR + encoder embeddings (Wave M4)
pip install audiotimm[whisper]

# + Training utilities
pip install audiotimm[train]

# + ONNX edge export
pip install audiotimm[onnx]

# Everything
pip install audiotimm[transformers,clap,speech,whisper,train,onnx]

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Quick Start

Classify a file

from audiotimm import Classifier

clf = Classifier.load()            # panns-cnn14 by default
result = clf.predict("siren.wav")

print(result.top(5))
# [("Siren", 0.93), ("Emergency vehicle", 0.81), ("Vehicle", 0.74), ...]

print(result.label)    # "Siren"
print(result.score)    # 0.93

Batch classification

results = clf.predict(["a.wav", "b.wav", "c.wav"])
print(results.labels())   # ["Dog", "Car horn", "Rain"]

Only results above a threshold

result.above(0.5)
# [("Siren", 0.93), ("Emergency vehicle", 0.81), ("Vehicle", 0.74)]

Get embeddings

emb = clf.embed("dog.wav")   # np.ndarray shape (2048,) for panns-cnn14

Switch models

# High accuracy transformer (requires pip install audiotimm[transformers])
clf = Classifier.load("ast-10-10")

# Lightweight 16 kHz variant of PANNs
clf = Classifier.load("panns-cnn14-16k")

CLI

# ── predict ──────────────────────────────────────────────────────────────
# Basic classification
audiotimm predict siren.wav

# Top-10 results
audiotimm predict siren.wav --top 10

# Show only labels above a confidence threshold
audiotimm predict siren.wav --threshold 0.3

# Use a specific model
audiotimm predict siren.wav --model ast-10-10

# Batch — processes all files, shows per-file results
audiotimm predict audio/*.wav --model panns-cnn14

# JSON output (single file or batch)
audiotimm predict siren.wav --json
audiotimm predict audio/*.wav --json --output results.jsonl

# Run on GPU
audiotimm predict siren.wav --model beats-iter3plus-as2m-cpt2 --device cuda

# ── embed ─────────────────────────────────────────────────────────────────
# Print embedding stats to stdout
audiotimm embed dog.wav

# Save single embedding as .npy
audiotimm embed dog.wav --output dog.npy

# Save batch as compressed .npz  (keys = file stems)
audiotimm embed audio/*.wav --output embeddings.npz

# Save as CSV (filename, dim_0, dim_1, …)
audiotimm embed audio/*.wav --output embeddings.csv

# ── list / info ───────────────────────────────────────────────────────────
# List all models
audiotimm list

# Filter by wave or task
audiotimm list --wave M1
audiotimm list --task tagging
audiotimm list --family beats

# Machine-readable JSON
audiotimm list --json

# Detailed card for one model
audiotimm info beats-iter3plus-as2m-cpt2
audiotimm info ast-10-10

# ── benchmark ─────────────────────────────────────────────────────────────
# Time 20 inference runs and print mean/median/min/max/std
audiotimm benchmark siren.wav --model panns-cnn14 --runs 20
audiotimm benchmark siren.wav --model ast-10-10 --device cuda

# ── version ───────────────────────────────────────────────────────────────
audiotimm --version

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Available Models

Wave M0 — CNN Taggers (core, no extras)

Zoo ID	Architecture	SR	Classes	mAP	Notes
panns-cnn14 ⭐	CNN14	32 kHz	527	0.431	Default model
panns-cnn14-16k	CNN14	16 kHz	527	0.438	Slightly higher mAP
yamnet	MobileNetV1	16 kHz	521	—	PyTorch path coming in v0.2

Wave M1 — Transformer Taggers pip install audiotimm[transformers]

Zoo ID	Architecture	SR	Classes	mAP	Notes
ast-10-10 ⭐	Audio Spectrogram Transformer	16 kHz	527	0.459	Default AST
ast-16-16	AST (larger patches)	16 kHz	527	0.442	Faster
ast-speechcommands	AST	16 kHz	35	—	Keyword spotting
htsat-audioset	HTS-AT (Swin-style)	32 kHz	527	0.471	Also CLAP encoder
htsat-desed	HTS-AT	32 kHz	—	—	Sound event detection
audiomae-base-ft	AudioMAE (ViT-Base)	16 kHz	527	0.473	Facebook MAE
beats-iter3plus-as2m-cpt2	BEATs	16 kHz	527	0.486	SOTA mAP

Wave M2 — Zero-Shot CLAP pip install audiotimm[clap]

Zoo ID	Variant	SR	Notes
clap-laion-fused ⭐	LAION HTSAT + feature fusion	48 kHz	Handles long audio
clap-laion-unfused	LAION HTSAT	48 kHz
clap-laion-music-audioset	Music + AudioSet trained	48 kHz	ESC-50 ≈ 90.1%
clap-ms-2023 ⭐	MS-CLAP HTSAT + GPT-2	44.1 kHz	Stronger text encoder
clap-ms-2022	MS-CLAP CNN14 + BERT	44.1 kHz
clap-ms-clapcap	MS-CLAP + captioning head	44.1 kHz	Audio → text captions

Wave M3 — Speech SSL Backbones pip install audiotimm[speech]

Zoo ID	Architecture	SR	Output
wav2vec2-base	Wav2Vec2 Base	16 kHz	Frame embeddings
wav2vec2-large-xlsr	XLS-R 300M (128 languages)	16 kHz	Multilingual
hubert-large-ll60k	HuBERT Large	16 kHz	Strong SER backbone
wavlm-large ⭐	WavLM Large	16 kHz	Best for speaker tasks
wavlm-base-plus-sv	WavLM + SV head	16 kHz	Speaker verification

Wave M4 — Whisper pip install audiotimm[whisper]

Zoo ID	Size	Languages	Notes
whisper-base	Base	99	Fast, general
whisper-large-v3 ⭐	Large v3	99	Best accuracy
whisper-large-v3-turbo	Large v3 Turbo	99	Fast + accurate
whisper-distil-large-v3	Distil Large v3	1 (EN)	~2× faster

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Zero-Shot Classification (Wave M2)

Classify audio into any labels you define — no training needed:

from audiotimm import ZeroShotClassifier   # coming in Phase 2

zs = ZeroShotClassifier.load("clap-laion-fused")
result = zs.classify(
    "clip.wav",
    labels=["dog barking", "car horn", "rain", "crowd applause"]
)
# -> [("rain", 0.81), ("crowd applause", 0.10), ...]

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Plugin API — Register Custom Models

from audiotimm import register_model
from audiotimm.models._base import ModelAdapter
from audiotimm.core.registry import ModelSpec

@register_model("my-bird-net")
class BirdNet(ModelAdapter):

    @classmethod
    def spec(cls):
        return ModelSpec(
            name="",           # filled by decorator
            family="custom",
            adapter_factory=cls,
            checkpoint="./weights/birdnet.pt",
            sample_rate=22050,
            n_classes=500,
            embed_dim=512,
            task="tagging",
            wave="M0",
        )

    def predict(self, waveform):
        ...  # return {label: score} dict

# Now available everywhere
from audiotimm import Classifier
clf = Classifier.load("my-bird-net")

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Project Roadmap

Phase 1  ✅  Core engine + PANNs CNN family (Wave M0)
Phase 2  ✅  Wave M1 — AST, AudioMAE, HTS-AT, BEATs (transformer taggers)
Phase 3  ·   Wave M2 — CLAP zero-shot (LAION + MS)
Phase 4  ·   Embeddings & similarity search
Phase 5  ·   Sound Event Detection timeline
Phase 6  ·   Wave M3 — Wav2Vec2, HuBERT, WavLM speech SSL
Phase 7  ·   Training & fine-tuning (Trainer API)
Phase 8  ·   Wave M4 — Whisper ASR + encoder embeddings
Phase 9  ·   Evaluation & explainability (Grad-CAM on mel-spectrogram)
Phase 10 ·   Domain packs (bioacoustics, security, health, music, speech)
Phase 11 ·   Streaming / real-time inference
Phase 12 ·   ONNX / TFLite edge export
Phase 13 ·   XenAudio integration + plugin API

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Architecture

audiotimm/
├── core/
│   ├── classifier.py    # Classifier.load(), predict(), embed()
│   ├── result.py        # PredictionResult, BatchResult
│   └── registry.py      # ModelRegistry singleton + @register_model
├── models/
│   ├── _base.py         # ModelAdapter ABC
│   ├── panns.py         # Wave M0 — CNN14 family
│   ├── yamnet.py        # Wave M0 — YAMNet (stub)
│   ├── ast.py           # Wave M1 — AST (coming)
│   ├── beats.py         # Wave M1 — BEATs (coming)
│   ├── htsat.py         # Wave M1+M2 — HTS-AT (coming)
│   ├── audiomae.py      # Wave M1 — AudioMAE (coming)
│   ├── clap.py          # Wave M2 — LAION + MS-CLAP (coming)
│   ├── wav2vec2.py      # Wave M3 (coming)
│   ├── hubert.py        # Wave M3 (coming)
│   ├── wavlm.py         # Wave M3 (coming)
│   └── whisper.py       # Wave M4 (coming)
├── utils/
│   ├── audio.py         # load_audio(), pad_or_trim()
│   └── download.py      # cached downloader (~/.cache/audiotimm/)
└── cli.py               # `audiotimm predict` / `audiotimm list`

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Design Principles

• Lazy everything — weights download on first predict(), not on import.
• One result type — PredictionResult everywhere; switching models never breaks your code.
• Lean core — torch + torchaudio + numpy only for the default model; every heavy dep is behind an optional extra.
• Registry-first — every model is a registry entry; custom models slot in with @register_model.
• Immutable results — PredictionResult is read-only; safe to cache and pass around.

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Contributing

git clone https://github.com/shubham10divakar/audiotimm
cd audiotimm
pip install -e ".[dev]"
pytest tests/

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License

Apache 2.0. Model weights are subject to their respective upstream licenses — see PLAN.md Appendix A for per-checkpoint license notes.

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