diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..bf7dd8b5d020fe98fb5c58f54a7ccd7376b10aa0 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,3 +1,5 @@ + +# Default *.7z filter=lfs diff=lfs merge=lfs -text *.arrow filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 51f92a3e80161ebb46efac5d3c9a7d1cdea3c2f6..b4e16af1a125e0ae44c8448490f50bd3191cbe03 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,70 @@ --- -title: Conversational Webgpu -emoji: ⚡ -colorFrom: yellow -colorTo: green +emoji: "\U0001F4AC" sdk: static pinned: false +license: mit +title: Realtime Conversational WebGPU (Vue) +colorFrom: purple +colorTo: indigo +models: + - HuggingFaceTB/SmolLM2-1.7B-Instruct + - onnx-community/whisper-base + - onnx-community/silero-vad +short_description: Yet another Realtime Conversational WebGPU --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +

Realtime Conversational WebGPU (Vue)

+ +

+ [Try it] +

+ +> Heavily inspired by [WebGPU Video Object Detection - a Hugging Face Space by WebML Community](https://huggingface.co/spaces/webml-community/webgpu-video-object-detection) + +# Realtime Conversational WebGPU + +## Getting Started + +Follow the steps below to set up and run the application. + +### 1. Clone the Repository + +Clone the examples repository from GitHub: + +```sh +git clone https://github.com/proj-airi/webai-examples.git +``` + +### 2. Navigate to the Project Directory + +Change your working directory to the `conversational-webgpu` folder: + +```sh +cd apps/conversational-webgpu +``` + +### 3. Install Dependencies + +Install the necessary dependencies using npm: + +```sh +npm i +``` + +### 4. Run the Development Server + +Start the development server: + +```sh +npm run dev +``` + +The application should now be running locally. Open your browser and go to `http://localhost:5175` to see it in action. + +## Acknowledgements + +Great thanks to what WebML Community have done. + +> [Source code](https://huggingface.co/spaces/webml-community/conversational-webgpu) + +> [UI inspiration](https://app.sesame.com/) diff --git a/assets/index-DGmKQH7N.js b/assets/index-DGmKQH7N.js new file mode 100644 index 0000000000000000000000000000000000000000..a21e4ad0d7d7f4b755fb3ed1c12721208c34a0b6 --- /dev/null +++ b/assets/index-DGmKQH7N.js @@ -0,0 +1,20 @@ +var ru=Object.defineProperty;var su=(e,t,n)=>t in e?ru(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var tt=(e,t,n)=>su(e,typeof t!="symbol"?t+"":t,n);(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const s of document.querySelectorAll('link[rel="modulepreload"]'))r(s);new MutationObserver(s=>{for(const i of s)if(i.type==="childList")for(const o of 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a/assets/play-worklet-CqUYQx_r.js b/assets/play-worklet-CqUYQx_r.js new file mode 100644 index 0000000000000000000000000000000000000000..a067fe973bef50407505c8cd9657cbd6152e07d1 --- /dev/null +++ b/assets/play-worklet-CqUYQx_r.js @@ -0,0 +1 @@ +var c=Object.defineProperty;var l=(s,e,t)=>e in s?c(s,e,{enumerable:!0,configurable:!0,writable:!0,value:t}):s[e]=t;var n=(s,e,t)=>l(s,typeof e!="symbol"?e+"":e,t);class d extends AudioWorkletProcessor{constructor(){super();n(this,"bufferQueue",[]);n(this,"currentChunkOffset",0);n(this,"hadData",!1);this.bufferQueue=[],this.currentChunkOffset=0,this.hadData=!1,this.port.onmessage=t=>{const r=t.data;r instanceof Float32Array?(this.hadData=!0,this.bufferQueue.push(r)):r==="stop"&&(this.bufferQueue=[],this.currentChunkOffset=0)}}process(t,r){const f=r[0][0];if(!f)return!0;const h=f.length;let u=0;for(this.hadData&&this.bufferQueue.length===0&&(this.port.postMessage({type:"playback_ended"}),this.hadData=!1);u0){const a=this.bufferQueue[0],o=a.length-this.currentChunkOffset,i=Math.min(o,h-u);f.set(a.subarray(this.currentChunkOffset,this.currentChunkOffset+i),u),this.currentChunkOffset+=i,u+=i,this.currentChunkOffset>=a.length&&(this.bufferQueue.shift(),this.currentChunkOffset=0)}else f.fill(0,u),u=h;return!0}}registerProcessor("buffered-audio-worklet-processor",d); diff --git a/assets/vad-processor-0sEQXaXZ.js b/assets/vad-processor-0sEQXaXZ.js new file mode 100644 index 0000000000000000000000000000000000000000..67392775fb9dd8584f91e8c2d1444d7f7b249e6b --- /dev/null +++ b/assets/vad-processor-0sEQXaXZ.js @@ -0,0 +1 @@ +let s=0;const r=new Float32Array(512);class l extends AudioWorkletProcessor{process(o,n,f){const e=o[0][0];if(!e)return!1;if(e.length>512)this.port.postMessage({buffer:e});else{const t=512-s;e.length>=t?(r.set(e.subarray(0,t),s),this.port.postMessage({buffer:r}),r.fill(0),r.set(e.subarray(t),0),s=e.length-t):(r.set(e,s),s+=e.length)}return!0}}registerProcessor("vad-processor",l); diff --git a/assets/worker-yoCrhISy.ts b/assets/worker-yoCrhISy.ts new file mode 100644 index 0000000000000000000000000000000000000000..b0fcd319be7203755cf4436a594fc06b5ee3fa14 --- /dev/null +++ b/assets/worker-yoCrhISy.ts @@ -0,0 +1,465 @@ +import type { + AutomaticSpeechRecognitionPipeline, + CausalLMOutputWithPast, + GPT2Tokenizer, + LlamaForCausalLM, + PreTrainedModel, + StoppingCriteriaList, +} from '@huggingface/transformers' +import type { Device, DType } from '@xsai-transformers/shared/types' +import type { GenerateOptions } from 'kokoro-js' +import type { + WorkerMessageEventError, + WorkerMessageEventInfo, + WorkerMessageEventOutput, + WorkerMessageEventProgress, + WorkerMessageEventSetVoiceResponse, + WorkerMessageEventStatus, +} from '../types/worker' + +import { + // VAD + AutoModel, + + AutoModelForCausalLM, + // LLM + AutoTokenizer, + InterruptableStoppingCriteria, + pipeline, + + // Speech recognition + Tensor, + TextStreamer, +} from '@huggingface/transformers' +import { isWebGPUSupported } from 'gpuu/webgpu' +import { KokoroTTS, TextSplitterStream } from 'kokoro-js' + +import { + EXIT_THRESHOLD, + INPUT_SAMPLE_RATE, + MAX_BUFFER_DURATION, + MAX_NUM_PREV_BUFFERS, + MIN_SILENCE_DURATION_SAMPLES, + MIN_SPEECH_DURATION_SAMPLES, + SPEECH_PAD_SAMPLES, + SPEECH_THRESHOLD, +} from '../constants' + +interface Message { + role: 'system' | 'user' | 'assistant' + content: string +} + +type Voices = GenerateOptions['voice'] +export type PretrainedConfig = NonNullable[1]>['config'] + +const whisperDtypeMap: Record = { + webgpu: { + encoder_model: 'fp32', + decoder_model_merged: 'fp32', + }, + wasm: { + encoder_model: 'fp32', + decoder_model_merged: 'q8', + }, +} + +const model_id = 'onnx-community/Kokoro-82M-v1.0-ONNX' +let voice: Voices | undefined +let silero_vad: PreTrainedModel +let transcriber: AutomaticSpeechRecognitionPipeline +let tts: KokoroTTS + +const SYSTEM_MESSAGE: Message = { + role: 'system', + content: + 'You\'re a helpful and conversational voice assistant. Keep your responses short, clear, and casual.', +} +let messages: Message[] = [SYSTEM_MESSAGE] +let past_key_values_cache: any = null +let stopping_criteria: InterruptableStoppingCriteria | null = null + +// Global audio buffer to store incoming audio +const BUFFER = new Float32Array(MAX_BUFFER_DURATION * INPUT_SAMPLE_RATE) +let bufferPointer = 0 + +// Initial state for VAD +const sr = new Tensor('int64', [INPUT_SAMPLE_RATE], []) +let state = new Tensor('float32', new Float32Array(2 * 1 * 128), [2, 1, 128]) + +// Whether we are in the process of adding audio to the buffer +let isRecording = false +let isPlaying = false // new flag + +let tokenizer: GPT2Tokenizer +let llm: LlamaForCausalLM + +const prevBuffers: Float32Array[] = [] + +export async function loadModels() { + tts = await KokoroTTS.from_pretrained(model_id, { + dtype: 'fp32', + device: 'webgpu', + }) + + const device = 'webgpu' + globalThis.postMessage({ type: 'info', data: { message: `Using device: "${device}"` } } satisfies WorkerMessageEventInfo) + globalThis.postMessage({ type: 'info', data: { message: 'Loading models...', duration: 'until_next' } } satisfies WorkerMessageEventInfo) + + // Load models + silero_vad = await AutoModel.from_pretrained( + 'onnx-community/silero-vad', + { + config: { model_type: 'custom' } as PretrainedConfig, + dtype: 'fp32', // Full-precision + progress_callback: progress => globalThis.postMessage({ type: 'progress', data: { message: progress } } satisfies WorkerMessageEventProgress), + }, + ).catch((error: Error) => { + globalThis.postMessage({ type: 'error', data: { error, message: error.message } } satisfies WorkerMessageEventError) + throw error + }) + + transcriber = await pipeline( + 'automatic-speech-recognition', + 'onnx-community/whisper-base', // or "onnx-community/moonshine-base-ONNX", + { + device, + dtype: whisperDtypeMap[device as keyof typeof whisperDtypeMap], + progress_callback: progress => globalThis.postMessage({ type: 'progress', data: { message: progress } } satisfies WorkerMessageEventProgress), + }, + ).catch((error: Error) => { + globalThis.postMessage({ type: 'error', data: { error, message: error.message } } satisfies WorkerMessageEventError) + throw error + }) + + await transcriber(new Float32Array(INPUT_SAMPLE_RATE)) // Compile shaders + + llm = await AutoModelForCausalLM.from_pretrained( + 'HuggingFaceTB/SmolLM2-1.7B-Instruct', + { + dtype: await isWebGPUSupported() ? 'q4f16' : 'int8', + device: await isWebGPUSupported() ? 'webgpu' : 'wasm', + progress_callback: progress => globalThis.postMessage({ type: 'progress', data: { message: progress } } satisfies WorkerMessageEventProgress), + }, + ).catch((error: Error) => { + globalThis.postMessage({ type: 'error', data: { error, message: error.message } } satisfies WorkerMessageEventError) + throw error + }) + + tokenizer = await AutoTokenizer.from_pretrained( + 'HuggingFaceTB/SmolLM2-1.7B-Instruct', + ).catch((error: Error) => { + globalThis.postMessage({ type: 'error', data: { error, message: error.message } } satisfies WorkerMessageEventError) + throw error + }) + + await llm.generate({ ...tokenizer('x'), max_new_tokens: 1 }) // Compile shaders + + globalThis.postMessage({ + type: 'status', + data: { + status: 'ready', + message: 'Ready!', + voices: tts.voices, + }, + } as WorkerMessageEventStatus) +} + +loadModels() + +/** + * Perform Voice Activity Detection (VAD) + * @param buffer The new audio buffer + * @returns `true` if the buffer is speech, `false` otherwise. + */ +async function vad(buffer?: Float32Array): Promise { + if (!buffer) { + // Possibly closed or interrupted + return false + } + + const input = new Tensor('float32', buffer, [1, buffer.length]) + + const { stateN, output } = await silero_vad({ input, sr, state }) + state = stateN // Update state + + const isSpeech = output.data[0] + + // Use heuristics to determine if the buffer is speech or not + return ( + // Case 1: We are above the threshold (definitely speech) + isSpeech > SPEECH_THRESHOLD + // Case 2: We are in the process of recording, and the probability is above the negative (exit) threshold + || (isRecording && isSpeech >= EXIT_THRESHOLD) + ) +} + +interface SpeechData { + start: number + end: number + duration: number +} + +type BatchEncodingItem = number[] | number[][] | Tensor +/** + * Holds the output of the tokenizer's call function. + */ +interface BatchEncoding { + /** + * List of token ids to be fed to a model. + */ + input_ids: BatchEncodingItem + /** + * List of indices specifying which tokens should be attended to by the model. + */ + attention_mask: BatchEncodingItem + /** + * List of token type ids to be fed to a model. + */ + token_type_ids?: BatchEncodingItem +} + +/** + * Transcribe the audio buffer + * @param buffer The audio buffer + * @param _data Additional data + */ +async function speechToSpeech(buffer: Float32Array, _data: SpeechData): Promise { + isPlaying = true + + // 1. Transcribe the audio from the user + const result = await transcriber(buffer) + const text = (result as { text: string }).text.trim() + + if (['', '[BLANK_AUDIO]'].includes(text)) { + // If the transcription is empty or a blank audio, we skip the rest of the processing + return + } + + messages.push({ role: 'user', content: text }) + + // Set up text-to-speech streaming + const splitter = new TextSplitterStream() + const stream = tts!.stream(splitter, { voice }); + (async () => { + for await (const { text, audio } of stream) { + globalThis.postMessage({ type: 'output', data: { text, result: audio } } satisfies WorkerMessageEventOutput) + } + })() + + // 2. Generate a response using the LLM + const inputs = tokenizer.apply_chat_template(messages, { + add_generation_prompt: true, + return_dict: true, + }) as BatchEncoding + + const streamer = new TextStreamer(tokenizer, { + skip_prompt: true, + skip_special_tokens: true, + callback_function: (text: string) => { + splitter.push(text) + }, + token_callback_function: () => {}, + }) + + stopping_criteria = new InterruptableStoppingCriteria() + type GenerationFunctionParameters = Parameters[0] & Record + + const generatedRes = await llm.generate({ + ...inputs, + past_key_values: past_key_values_cache, + do_sample: false, // TODO: do_sample: true is bugged (invalid data location on top-k sample) + max_new_tokens: 1024, + streamer, + stopping_criteria: stopping_criteria as unknown as StoppingCriteriaList, + return_dict_in_generate: true, + } as GenerationFunctionParameters) + + const { past_key_values, sequences } = generatedRes as CausalLMOutputWithPast & { sequences: Tensor } + past_key_values_cache = past_key_values + + // Finally, close the stream to signal that no more text will be added. + splitter.close() + + const decoded = tokenizer.batch_decode( + // TODO: fix null as any + sequences.slice(null, [(inputs.input_ids as Tensor).dims[1], null as any]), + { skip_special_tokens: true }, + ) + + messages.push({ role: 'assistant', content: decoded[0] }) +} + +// Track the number of samples after the last speech chunk +let postSpeechSamples = 0 +function resetAfterRecording(offset = 0): void { + globalThis.postMessage({ + type: 'status', + data: { + status: 'recording_end', + message: 'Transcribing...', + duration: 'until_next', + }, + } satisfies WorkerMessageEventStatus) + + BUFFER.fill(0, offset) + bufferPointer = offset + isRecording = false + postSpeechSamples = 0 +} + +function dispatchForTranscriptionAndResetAudioBuffer(overflow?: Float32Array): void { + // Get start and end time of the speech segment, minus the padding + const now = Date.now() + const end + = now - ((postSpeechSamples + SPEECH_PAD_SAMPLES) / INPUT_SAMPLE_RATE) * 1000 + const start = end - (bufferPointer / INPUT_SAMPLE_RATE) * 1000 + const duration = end - start + const overflowLength = overflow?.length ?? 0 + + // Send the audio buffer to the worker + const buffer = BUFFER.slice(0, bufferPointer + SPEECH_PAD_SAMPLES) + + const prevLength = prevBuffers.reduce((acc, b) => acc + b.length, 0) + const paddedBuffer = new Float32Array(prevLength + buffer.length) + let offset = 0 + for (const prev of prevBuffers) { + paddedBuffer.set(prev, offset) + offset += prev.length + } + paddedBuffer.set(buffer, offset) + speechToSpeech(paddedBuffer, { start, end, duration }) + + // Set overflow (if present) and reset the rest of the audio buffer + if (overflow) { + BUFFER.set(overflow, 0) + } + resetAfterRecording(overflowLength) +} + +globalThis.onmessage = async (event: MessageEvent) => { + const { type, buffer } = event.data + + // refuse new audio while playing back + if (type === 'audio' && isPlaying) + return + + switch (type) { + case 'start_call': { + const name = tts!.voices[voice ?? 'af_heart']?.name ?? 'Heart' + greet(`Hey there, my name is ${name}! How can I help you today?`) + return + } + case 'end_call': + messages = [SYSTEM_MESSAGE] + past_key_values_cache = null + break + case 'interrupt': + stopping_criteria?.interrupt() + return + case 'set_voice': + voice = event.data.voice + + globalThis.postMessage({ + type: 'set_voice_response', + data: { + ok: true, + }, + } satisfies WorkerMessageEventSetVoiceResponse) + + return + case 'playback_ended': + isPlaying = false + return + } + + const wasRecording = isRecording // Save current state + const isSpeech = await vad(buffer) + + if (!wasRecording && !isSpeech) { + // We are not recording, and the buffer is not speech, + // so we will probably discard the buffer. So, we insert + // into a FIFO queue with maximum size of PREV_BUFFER_SIZE + if (prevBuffers.length >= MAX_NUM_PREV_BUFFERS) { + // If the queue is full, we discard the oldest buffer + prevBuffers.shift() + } + prevBuffers.push(buffer) + return + } + + const remaining = BUFFER.length - bufferPointer + if (buffer.length >= remaining) { + // The buffer is larger than (or equal to) the remaining space in the global buffer, + // so we perform transcription and copy the overflow to the global buffer + BUFFER.set(buffer.subarray(0, remaining), bufferPointer) + bufferPointer += remaining + + // Dispatch the audio buffer + const overflow = buffer.subarray(remaining) + dispatchForTranscriptionAndResetAudioBuffer(overflow) + return + } + else { + // The buffer is smaller than the remaining space in the global buffer, + // so we copy it to the global buffer + BUFFER.set(buffer, bufferPointer) + bufferPointer += buffer.length + } + + if (isSpeech) { + if (!isRecording) { + // Indicate start of recording + globalThis.postMessage({ + type: 'status', + data: { + status: 'recording_start', + message: 'Listening...', + duration: 'until_next', + }, + } satisfies WorkerMessageEventStatus) + } + + // Start or continue recording + isRecording = true + postSpeechSamples = 0 // Reset the post-speech samples + + return + } + + postSpeechSamples += buffer.length + + // At this point we're confident that we were recording (wasRecording === true), but the latest buffer is not speech. + // So, we check whether we have reached the end of the current audio chunk. + if (postSpeechSamples < MIN_SILENCE_DURATION_SAMPLES) { + // There was a short pause, but not long enough to consider the end of a speech chunk + // (e.g., the speaker took a breath), so we continue recording + return + } + + if (bufferPointer < MIN_SPEECH_DURATION_SAMPLES) { + // The entire buffer (including the new chunk) is smaller than the minimum + // duration of a speech chunk, so we can safely discard the buffer. + resetAfterRecording() + return + } + + dispatchForTranscriptionAndResetAudioBuffer() +} + +function greet(text: string): void { + isPlaying = true + + const splitter = new TextSplitterStream() + const stream = tts!.stream(splitter, { voice }); + + (async () => { + for await (const { text: chunkText, audio } of stream) { + globalThis.postMessage({ type: 'output', data: { text: chunkText, result: audio } } satisfies WorkerMessageEventOutput) + } + })() + + splitter.push(text) + splitter.close() + messages.push({ role: 'assistant', content: text }) +} diff --git a/favicon-96x96.png b/favicon-96x96.png new file mode 100644 index 0000000000000000000000000000000000000000..a70eda1afe66bcdb77cedc2cdae6ac440753e4c5 Binary files /dev/null and b/favicon-96x96.png differ diff --git a/favicon.svg b/favicon.svg new file mode 100644 index 0000000000000000000000000000000000000000..17940af4792c2c855fcb5b330db53786f28ddc5a --- /dev/null +++ b/favicon.svg @@ -0,0 +1,8 @@ + + + + + + \ No newline at end of file diff --git a/index.html b/index.html index b0c4b3666032a737f3903db53e6a8a9272483e28..dee57150efad2435bcf48cd5da4d12b5b3b6d052 100644 --- a/index.html +++ b/index.html @@ -1,19 +1,24 @@ - - - - - My static Space - - - -
-

Welcome to your static Space!

-

You can modify this app directly by editing index.html in the Files and versions tab.

-

- Also don't forget to check the - Spaces documentation. -

-
- + + + + Realtime Conversational WebGPU (Vue) + + + + + + + + +
+ + diff --git a/style.css b/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -}