Loading
Tem um workflow do ComfyUI? Gere uma documentação legível direto no navegador.Torne um workflow legível.Experimentar agora ↗
CoreML conversion of speechbrain/lang-id-voxlingua107-ecapa for native inference on Apple Silicon (macOS 14+ / iOS 17+).
Fonte do modelo
Descrição da fonte
CoreML conversion of speechbrain/lang-id-voxlingua107-ecapa for native inference on Apple Silicon (macOS 14+ / iOS 17+).
Identifies 107 spoken languages from log-mel spectrogram. No Python required at runtime.
Fontes
1 fonteVerificado 13 de set.
Artefatos de modelo
1 artefatoEcapaTdnnLid107.mlpackage/Data/com.apple.CoreML/weights/weight.bin
bin · 81,2 MB · SHA-256 089e9e451d25…a810 · Hugging Face
BaixarTrechos de fonte
2 trechos| Property | Value |
|---|
| Source | speechbrain/lang-id-voxlingua107-ecapa |
| Architecture | ECAPA-TDNN |
| Languages | 107 |
| Input | Log-mel spectrogram [1, T, 60] float32 |
| Output | Log-probabilities [1, 107] |
| Size | 81 MB |
| Precision | FP32 |
| Min deployment | macOS 14 / iOS 17 |
| Compute units | CPU + GPU (ANE not used) |
Tested on Apple Silicon (M1, Metal GPU, .cpuAndGPU):
| Audio | Predicted | Confidence | Inference Time | Mel Time |
|---|---|---|---|---|
| Russian (10s) | ru: Russian | 99.7% | 0.017s | 0.019s |
| English (30s) | en: English | 98.6% | 2.0s | 0.053s |
15-50x faster than MMS-LID-256 with comparable accuracy.
import CoreML
let compiledURL = try MLModel.compileModel(at: URL(fileURLWithPath: "EcapaTdnnLid107.mlpackage"))
let config = MLModelConfiguration()
config.computeUnits = .cpuAndGPU // ANE provides no benefit for this model
let model = try MLModel(contentsOf: compiledURL, configuration: config)
// melFrames: [[Float]] — log-mel spectrogram [T][60]
let T = melFrames.count
let inputArray = try MLMultiArray(shape: [1, NSNumber(value: T), 60], dataType: .float32)
for t in 0..<T {
for f in 0..<60 {
inputArray[[0, NSNumber(value: t), NSNumber(value: f)]] = NSNumber(value: melFrames[t][f])
}
}
let input = try MLDictionaryFeatureProvider(dictionary: [
"mel_features": MLFeatureValue(multiArray: inputArray),
])
let output = try model.prediction(from: input)
The model expects a log-mel spectrogram computed with SpeechBrain-compatible parameters. This must be computed on-device (not baked into the CoreML model).
| Parameter | Value |
|---|---|
| Sample rate | 16000 Hz |
| n_fft | 400 |
| hop_length | 160 (10ms) |
| win_length | 400 (25ms) |
| n_mels | 60 |
| Window | Hamming (periodic) |
| Center padding | Yes (zero-pad) |
| Filterbank | SpeechBrain symmetric triangular |
| Log scale | 10 * log10(clamp(x, 1e-10)) |
| Dynamic range | top_db=80 per sequence |
vDSP_fft_zrip silently computes wrong results. Use manual DFT via cblas_sgemv with precomputed twiddle factors.band[m] = hz[m+1] - hz[m] applied equally to both sides.vDSP_hamm_window generates symmetric windows. Create N+1 symmetric, take first N for periodic.EcapaTdnnLid107.mlpackage/ — CoreML modelecapa_tdnn_lid107_labels.json — language label mapping (index → "code: Name")Converted via torch.jit.trace → coremltools 9.0. See conversion script.
Complete Swift CLI with audio loading, mel spectrogram (Accelerate/vDSP), and inference: github.com/beshkenadze/lid-bench
Apache 2.0 (same as the original model)
--- library_name: coreml tags: - coreml - audio - language-identification - apple-silicon - swift - ecapa-tdnn - speechbrain license: apache-2.0 base_model: speechbrain/lang-id-voxlingua107-ecapa pipeline_tag: audio-classification --- # ECAPA-TDNN VoxLingua107 CoreML CoreML conversion of [speechbrain/lang-id-voxlingua107-ecapa](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa) for native inference on Apple Silicon (macOS 14+ / iOS 17+). Identifies **107 spoken languages** from log-mel spectrogram. No Python required at runtime. ## Model Details | Property | Value | |----------|-------| | Source | [speechbrain/lang-id-voxlingua107-ecapa](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa) | | Architecture | ECAPA-TDNN | | Languages | 107 | | Input | Log-mel spectrogram `[1, T, 60]` float32 | | Output | Log-probabilities `[1, 107]` | | Size | 81 MB | | Precision | FP32 | | Min deployment | macOS 14 / iOS 17 | | Compute units | **CPU + GPU** (ANE not used) | ## Benchmark Results Tested on Apple Silicon (M1, Metal GPU, `.cpuAndGPU`): | Audio | Predicted | Confidence | Inference Time | Mel Time | |-------|-----------|------------|----------------|----------| | Russian (10s) | ru: Russian | 99.7% | 0.017s | 0.019s | | English (30s) | en: English | 98.6% | 2.0s | 0.053s | 15-50x faster than MMS-LID-256 with comparable accuracy. ## Usage (Swift) ```swift import CoreML let compiledURL = try MLModel.compileModel(at: URL(fileURLWithPath: "EcapaTdnnLid107.mlpackage")) let config = MLModelConfiguration() config.computeUnits = .cpuAndGPU // ANE provides no benefit for this model let model = try MLModel(contentsOf: compiledURL, configuration: config) // melFrames: [[Float]] — log-mel spectrogram [T][60] let T = melFrames.count let inputArray = try MLMultiArray(shape: [1, NSNumber(value: T), 60], dataType: .float32) for t in 0..<T { for f in 0..<60 { inputArray[[0, NSNumber(value: t), NSNumber(value: f)]] = NSNumber(value: melFrames[t][f]) } } let input = try MLDictionaryFeatureProvider(dictionary: [ "mel_features": MLFeatureValue(multiArray: inputArray), ]) let output = try model.prediction(from: input) ``` ## Mel Spectrogram The model expects a log-mel spectrogram computed with SpeechBrain-compatible parameters. **This must be computed on-device** (not baked into the CoreML model). | Parameter | Value | |-----------|-...
Source context: 8 downloads · 0 likes · Pipeline audio-classification · Library coreml · Repo beshkenadze/lang-id-voxlingua107-ecapa-coreml