|
| 1 | +const core = globalThis.Deno.core; |
1 | 2 | import { InferenceSession, Tensor } from 'ext:ai/onnxruntime/onnx.js';
|
2 | 3 |
|
3 | 4 | const DEFAULT_HUGGING_FACE_OPTIONS = {
|
4 |
| - hostname: 'https://huggingface.co', |
5 |
| - path: { |
6 |
| - template: '{REPO_ID}/resolve/{REVISION}/onnx/{MODEL_FILE}?donwload=true', |
7 |
| - revision: 'main', |
8 |
| - modelFile: 'model_quantized.onnx', |
9 |
| - }, |
| 5 | + hostname: 'https://huggingface.co', |
| 6 | + path: { |
| 7 | + template: '{REPO_ID}/resolve/{REVISION}/onnx/{MODEL_FILE}?donwload=true', |
| 8 | + revision: 'main', |
| 9 | + modelFile: 'model_quantized.onnx', |
| 10 | + }, |
10 | 11 | };
|
11 | 12 |
|
12 | 13 | /**
|
13 | 14 | * An user friendly API for onnx backend
|
14 | 15 | */
|
15 | 16 | class UserInferenceSession {
|
16 |
| - inner; |
| 17 | + inner; |
17 | 18 |
|
18 |
| - id; |
19 |
| - inputs; |
20 |
| - outputs; |
| 19 | + id; |
| 20 | + inputs; |
| 21 | + outputs; |
21 | 22 |
|
22 |
| - constructor(session) { |
23 |
| - this.inner = session; |
| 23 | + constructor(session) { |
| 24 | + this.inner = session; |
24 | 25 |
|
25 |
| - this.id = session.sessionId; |
26 |
| - this.inputs = session.inputNames; |
27 |
| - this.outputs = session.outputNames; |
28 |
| - } |
29 |
| - |
30 |
| - static async fromUrl(modelUrl) { |
31 |
| - if (modelUrl instanceof URL) { |
32 |
| - modelUrl = modelUrl.toString(); |
33 |
| - } |
34 |
| - |
35 |
| - const encoder = new TextEncoder(); |
36 |
| - const modelUrlBuffer = encoder.encode(modelUrl); |
37 |
| - const session = await InferenceSession.fromBuffer(modelUrlBuffer); |
| 26 | + this.id = session.sessionId; |
| 27 | + this.inputs = session.inputNames; |
| 28 | + this.outputs = session.outputNames; |
| 29 | + } |
38 | 30 |
|
39 |
| - return new UserInferenceSession(session); |
| 31 | + static async fromUrl(modelUrl) { |
| 32 | + if (modelUrl instanceof URL) { |
| 33 | + modelUrl = modelUrl.toString(); |
40 | 34 | }
|
41 | 35 |
|
42 |
| - static async fromHuggingFace(repoId, opts = {}) { |
43 |
| - const hostname = opts?.hostname ?? DEFAULT_HUGGING_FACE_OPTIONS.hostname; |
44 |
| - const pathOpts = { |
45 |
| - ...DEFAULT_HUGGING_FACE_OPTIONS.path, |
46 |
| - ...opts?.path, |
47 |
| - }; |
48 |
| - |
49 |
| - const modelPath = pathOpts.template |
50 |
| - .replaceAll('{REPO_ID}', repoId) |
51 |
| - .replaceAll('{REVISION}', pathOpts.revision) |
52 |
| - .replaceAll('{MODEL_FILE}', pathOpts.modelFile); |
53 |
| - |
54 |
| - if (!URL.canParse(modelPath, hostname)) { |
55 |
| - throw Error(`[Invalid URL] Couldn't parse the model path: "${modelPath}"`); |
56 |
| - } |
57 |
| - |
58 |
| - return await UserInferenceSession.fromUrl(new URL(modelPath, hostname)); |
| 36 | + const encoder = new TextEncoder(); |
| 37 | + const modelUrlBuffer = encoder.encode(modelUrl); |
| 38 | + const session = await InferenceSession.fromBuffer(modelUrlBuffer); |
| 39 | + |
| 40 | + return new UserInferenceSession(session); |
| 41 | + } |
| 42 | + |
| 43 | + static async fromHuggingFace(repoId, opts = {}) { |
| 44 | + const hostname = opts?.hostname ?? DEFAULT_HUGGING_FACE_OPTIONS.hostname; |
| 45 | + const pathOpts = { |
| 46 | + ...DEFAULT_HUGGING_FACE_OPTIONS.path, |
| 47 | + ...opts?.path, |
| 48 | + }; |
| 49 | + |
| 50 | + const modelPath = pathOpts.template |
| 51 | + .replaceAll('{REPO_ID}', repoId) |
| 52 | + .replaceAll('{REVISION}', pathOpts.revision) |
| 53 | + .replaceAll('{MODEL_FILE}', pathOpts.modelFile); |
| 54 | + |
| 55 | + if (!URL.canParse(modelPath, hostname)) { |
| 56 | + throw Error( |
| 57 | + `[Invalid URL] Couldn't parse the model path: "${modelPath}"`, |
| 58 | + ); |
59 | 59 | }
|
60 | 60 |
|
61 |
| - async run(inputs) { |
62 |
| - const outputs = await core.ops.op_sb_ai_ort_run_session(this.id, inputs); |
| 61 | + return await UserInferenceSession.fromUrl(new URL(modelPath, hostname)); |
| 62 | + } |
63 | 63 |
|
64 |
| - // Parse to Tensor |
65 |
| - for (const key in outputs) { |
66 |
| - if (Object.hasOwn(outputs, key)) { |
67 |
| - const { type, data, dims } = outputs[key]; |
| 64 | + async run(inputs) { |
| 65 | + const outputs = await core.ops.op_ai_ort_run_session(this.id, inputs); |
68 | 66 |
|
69 |
| - outputs[key] = new UserTensor(type, data.buffer, dims); |
70 |
| - } |
71 |
| - } |
| 67 | + // Parse to Tensor |
| 68 | + for (const key in outputs) { |
| 69 | + if (Object.hasOwn(outputs, key)) { |
| 70 | + const { type, data, dims } = outputs[key]; |
72 | 71 |
|
73 |
| - return outputs; |
| 72 | + outputs[key] = new UserTensor(type, data.buffer, dims); |
| 73 | + } |
74 | 74 | }
|
| 75 | + |
| 76 | + return outputs; |
| 77 | + } |
75 | 78 | }
|
76 | 79 |
|
77 | 80 | class UserTensor extends Tensor {
|
78 |
| - constructor(type, data, dim) { |
79 |
| - super(type, data, dim); |
80 |
| - } |
| 81 | + constructor(type, data, dim) { |
| 82 | + super(type, data, dim); |
| 83 | + } |
81 | 84 |
|
82 |
| - async tryEncodeAudio(sampleRate) { |
83 |
| - return await core.ops.op_sb_ai_ort_encode_tensor_audio(this.data, sampleRate); |
84 |
| - } |
| 85 | + async tryEncodeAudio(sampleRate) { |
| 86 | + return await core.ops.op_ai_ort_encode_tensor_audio(this.data, sampleRate); |
| 87 | + } |
85 | 88 | }
|
86 | 89 |
|
87 | 90 | export default {
|
88 |
| - RawSession: UserInferenceSession, |
89 |
| - RawTensor: UserTensor, |
| 91 | + RawSession: UserInferenceSession, |
| 92 | + RawTensor: UserTensor, |
90 | 93 | };
|
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