Update README.md

README.md

@@ -49,6 +49,233 @@ quantized_model = quantize_dynamic(src_model_path, dst_model_path, weight_type=Q
 ```
 
 
-To use onnx need something,I'll add sample code later
+To use onnx need something,below is old sample
+```
+const _pad = "_";
+const _punctuation = ";:,.!?¡¿—…\"«»“” ";
+const _letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
+const _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ";
+
+// below code called Spread syntax
+const Symbols = [_pad, ..._punctuation, ..._letters, ..._letters_ipa];
+
+const SpaceId = Symbols.indexOf(' ');
+
+const symbolToId = {};
+const idToSymbol = {};
+
+// initialize symbolToId and  idToSymbol
+for (let i = 0; i < Symbols.length; i++) {
+symbolToId[Symbols[i]] = i;
+idToSymbol[i] = Symbols[i];
+}
+
+class MatchaOnnx {
+    constructor() {
+    }
+    async load_model(model_path,options={}){
+        this.session = await ort.InferenceSession.create(model_path,options);
+    }
+
+        get_output_names_html(){
+        if (typeof this.session=='undefined'){
+            return null
+        }
+        let outputNamesString = '[outputs]<br>';
+        const outputNames = this.session.outputNames;
+        for (let outputName of outputNames) {
+            console.log(outputName)
+            outputNamesString+=outputName+"<br>"
+        }
+        return outputNamesString.trim()
+    }
+
+    get_input_names_html(){
+        if (typeof this.session=='undefined'){
+            return null
+        }
+        
+        let inputNamesString = '[Inputs]<br>';
+        const inputNames = this.session.inputNames;
+
+        for (let inputName of inputNames) {
+            console.log(inputName)
+            inputNamesString+=inputName+"<br>"
+        }
+        return inputNamesString.trim()
+    }
+
+
+    processText(text) {
+    const x = this.intersperse(this.textToSequence(text));
+    const x_phones = this.sequenceToText(x);
+    const textList = [];
+    for (let i = 1; i < x_phones.length; i += 2) {
+    textList.push(x_phones[i]);
+    }
+
+    return {
+    x: x,
+    x_length: x.length,
+    x_phones: x_phones,
+    x_phones_label: textList.join(""),
+    };
+}
+
+
+    basicCleaners2(text, lowercase = false) {
+    if (lowercase) {
+    text = text.toLowerCase();
+    }
+    text = text.replace(/\s+/g, " ");
+    return text;
+}
+
+    textToSequence(text) {
+    const sequenceList = [];
+    const clean_text = this.basicCleaners2(text);
+    for (let i = 0; i < clean_text.length; i++) {
+    const symbol = clean_text[i];
+    sequenceList.push(symbolToId[symbol]);
+    }
+    return sequenceList;
+}
+
+    intersperse(sequence, item = 0) {
+    const sequenceList = [item];
+    for (let i = 0; i < sequence.length; i++) {
+    sequenceList.push(sequence[i]);
+    sequenceList.push(item);
+    }
+    return sequenceList;
+    }
+
+    sequenceToText(sequence) {
+    const textList = [];
+    for (let i = 0; i < sequence.length; i++) {
+    const symbol = idToSymbol[sequence[i]];
+    textList.push(symbol);
+    }
+    return textList.join("");
+}
+
+async infer(text, temperature, speed) {
+    console.log(this.session)
+    const dic = this.processText(text);
+console.log(`x:${dic.x.join(", ")}`);
+console.log(`x_length:${dic.x_length}`);
+console.log(`x_phones_label:${dic.x_phones_label}`);
+    
+// Prepare input tensors (assuming your ONNX Runtime library uses similar syntax)
+//const x_tensor = new this.session.Tensor('long', dic.x, [1, dic.x.length]);
+//const x_length_tensor = new this.session.Tensor('long', [dic.x.length], [1]);
+//const scales_tensor = new this.session.Tensor('float', [temperature, speed], [2]);
+
+const dataX = new BigInt64Array(dic.x.length)
+for (let i = 0; i < dic.x.length; i++) {
+    //console.log(dic.x[i])
+    dataX[i] = BigInt(dic.x[i]); // Convert each number to a BigInt
+    }
+const data_x_length = new BigInt64Array(1)
+data_x_length[0] = BigInt(dic.x_length)
+
+//const dataX = Int32Array.from([dic.x_length])
+const tensorX = new ort.Tensor('int64', dataX, [1, dic.x.length]);
+// const data_x_length = Int32Array.from([dic.x_length])
+const tensor_x_length = new ort.Tensor('int64', data_x_length, [1]);
+const data_scale = Float32Array.from( [temperature, speed])
+const tensor_scale = new ort.Tensor('float32', data_scale, [2]);
+
+
+// Run inference
+const output = await this.session.run({
+x: tensorX,
+x_lengths: tensor_x_length,
+scales: tensor_scale,
+});
+console.log(output)
+// Extract output (assuming your ONNX Runtime library uses similar syntax)
+const wav_array = output.wav.data;
+console.log(wav_array[0]);
+console.log(wav_array.length);
+
+const x_lengths_array = output.wav_lengths.data;
+console.log(x_lengths_array.join(", "));
+
+return wav_array;
+}
+
+
+}
+```
+convert to wav
+```
+
+
+function webWavPlay(f32array){
+    blob = float32ArrayToWav(f32array)
+    url = createObjectUrlFromBlob(blob)
+    console.log(url)
+    playAudioFromUrl(url)
+}
+
+function createObjectUrlFromBlob(blob) {
+    const url = URL.createObjectURL(blob);
+    return url;
+    }
+
+function playAudioFromUrl(url) {
+    const audio = new Audio(url);
+    audio.play().catch(error => console.error('Failed to play audio:', error));
+    }
+
+    
+//I copied
+//https://huggingface.co/spaces/k2-fsa/web-assembly-tts-sherpa-onnx-de/blob/main/app-tts.js
+        // this function is copied/modified from
+// https://gist.github.com/meziantou/edb7217fddfbb70e899e
+function float32ArrayToWav(floatSamples, sampleRate=22050) {
+        let samples = new Int16Array(floatSamples.length);
+        for (let i = 0; i < samples.length; ++i) {
+          let s = floatSamples[i];
+          if (s >= 1)
+            s = 1;
+          else if (s <= -1)
+            s = -1;
+      
+          samples[i] = s * 32767;
+        }
+      
+        let buf = new ArrayBuffer(44 + samples.length * 2);
+        var view = new DataView(buf);
+      
+        // http://soundfile.sapp.org/doc/WaveFormat/
+        //                   F F I R
+        view.setUint32(0, 0x46464952, true);               // chunkID
+        view.setUint32(4, 36 + samples.length * 2, true);  // chunkSize
+        //                   E V A W
+        view.setUint32(8, 0x45564157, true);  // format
+                                              //
+        //                      t m f
+        view.setUint32(12, 0x20746d66, true);          // subchunk1ID
+        view.setUint32(16, 16, true);                  // subchunk1Size, 16 for PCM
+        view.setUint32(20, 1, true);                   // audioFormat, 1 for PCM
+        view.setUint16(22, 1, true);                   // numChannels: 1 channel
+        view.setUint32(24, sampleRate, true);          // sampleRate
+        view.setUint32(28, sampleRate * 2, true);      // byteRate
+        view.setUint16(32, 2, true);                   // blockAlign
+        view.setUint16(34, 16, true);                  // bitsPerSample
+        view.setUint32(36, 0x61746164, true);          // Subchunk2ID
+        view.setUint32(40, samples.length * 2, true);  // subchunk2Size
+      
+        let offset = 44;
+        for (let i = 0; i < samples.length; ++i) {
+          view.setInt16(offset, samples[i], true);
+          offset += 2;
+        }
+      
+        return new Blob([view], {type: 'audio/wav'});
+      }
+```
 ### Audio
 I cut with VAD tools and denoise with resemble-enhance