diff --git a/hyperaudio-client-whisper-template.html b/hyperaudio-client-whisper-template.html
index 3eeb1d7..669adc3 100644
--- a/hyperaudio-client-whisper-template.html
+++ b/hyperaudio-client-whisper-template.html
@@ -8,32 +8,30 @@
       <span style="display:block; padding:16px" class="label-text">or</span>
       <input id="file-input" name="file" type="file" class="file-input w-full max-w-xs" />
       <hr class="my-2 h-0 border border-t-0 border-solid border-neutral-700 opacity-50 dark:border-neutral-200" />
-
-      <!--<label for="file-input" class="form-label">Which video/audio file should be transcribed?</label>
-      <input class="form-control" type="file" id="file-input" accept=".mp3,.wav,.mp4,.mov,.avi,.flv,.wmv,.mpeg,.mpg,.webm,.opus">
-      <div class="form-text">We only support audio and video files.</div>-->
     </div>
     <div class="mb-3">
       <label for="model-name-input" class="form-label label-text">Which model should be used?</label>
       <div>
         <select class="form-select select select-bordered w-full max-w-xs" aria-label="Default select example" id="model-name-input">
-          <option selected="" value="whisper-tiny.en">Whisper (Tiny) English</option>
-          <option value="whisper-tiny">Whisper (Tiny)</option>
-          <option value="whisper-base">Whisper (Base) English</option>
-          <option value="whisper-base">Whisper (Base)</option>
-          <option value="whisper-small.en">Whisper (Small) English</option>
-          <option value="whisper-small">Whisper (Small)</option>
+          <option selected="" value="Xenova/whisper-tiny.en">Whisper (Tiny) English</option>
+          <option value="Xenova/whisper-tiny">Whisper (Tiny)</option>
+          <option value="Xenova/whisper-base">Whisper (Base) English</option>
+          <option value="Xenova/whisper-base">Whisper (Base)</option>
+          <option value="Xenova/whisper-small.en">Whisper (Small) English</option>
+          <option value="Xenova/whisper-small">Whisper (Small)</option>
         </select>
       </div>
       <div class="form-text" style="font-size: 90%;">
         <p style="padding-top:16px">The models are listed in order of size. The larger the model, the more accurate it is – and slower to process.</p> 
         <p>The English models are slightly more accurate (for the English language only).</p>
+        <p>* Whisper running in the browser is currently in beta.</p>
       </div>
+      
     </div>
+    
     <div class="modal-action">
       <label id="form-submit-btn" for="transcribe-modal" class="btn btn-primary">TRANSCRIBE</label>
     </div>
-    <!--<button id="form-submit-btn" class="btn btn-primary" disabled="">Submit</button>-->
   </form>
 </div>
 </body>
diff --git a/hyperaudio-deepgram-modal.html b/hyperaudio-deepgram-modal.html
index 3b69327..73b140d 100644
--- a/hyperaudio-deepgram-modal.html
+++ b/hyperaudio-deepgram-modal.html
@@ -4,8 +4,6 @@
 <div id="deepgram-modal-template">
   <form id="deepgram-form" name="deepgram-form">
     <div class="flex flex-col gap-4 w-full">
-      <!--<label id="close-modal" for="transcribe-modal" class="btn btn-sm btn-circle absolute right-2 top-2">✕</label>
-      <h3 class="font-bold text-lg">Transcribe</h3>-->
       <input id="token" type="text" placeholder="Deepgram token" class="input input-bordered w-full max-w-xs" />
       <hr class="my-2 h-0 border border-t-0 border-solid border-neutral-700 opacity-50 dark:border-neutral-200" />
       <input id="media" type="text" placeholder="Link to media" class="input input-bordered w-full max-w-xs" />
diff --git a/index.html b/index.html
index 5b0d06a..5a942fd 100644
--- a/index.html
+++ b/index.html
@@ -1,5 +1,5 @@
 <!--  (C) The Hyperaudio Project. AGPL 3.0 @license: https://www.gnu.org/licenses/agpl-3.0.en.html -->
-<!--  Hyperaudio Lite Editor - Version 0.3 -->
+<!--  Hyperaudio Lite Editor - Version 0.4 -->
 
 <!--  Hyperaudio Lite Editor's source code is provided under a dual license model.
 
@@ -219,7 +219,7 @@ <h3 class="text-lg font-bold">Topics</h3>
               <h3 class="font-bold text-lg"  style="margin-bottom:16px">Transcribe</h3>
               <div role="tablist" class="tabs tabs-lifted">
 
-                <input type="radio" name="my_tabs_2" role="tab" class="tab" style="width:160px" aria-label="Whisper (Local)" checked />
+                <input type="radio" name="my_tabs_2" role="tab" class="tab" style="width:160px" aria-label="Whisper (Local) *" checked />
                 <div role="tabpanel" class="tab-content bg-base-100 border-base-300 rounded-box p-10">
                   <client-whisper-service></client-whisper-service>
                 </div>
diff --git a/js/hyperaudio-lite-editor-whisper.js b/js/hyperaudio-lite-editor-whisper.js
index 335d912..d396aaa 100644
--- a/js/hyperaudio-lite-editor-whisper.js
+++ b/js/hyperaudio-lite-editor-whisper.js
@@ -1,5 +1,5 @@
 /*! (C) The Hyperaudio Project. MIT @license: en.wikipedia.org/wiki/MIT_License. */
-/*! Version 0.0.4 */
+/*! Version 0.0.5 */
 
 
 class WhisperService extends HTMLElement {
@@ -49,39 +49,9 @@ function loadWhisperClient(modal) {
   const resultsContainer = document.getElementById("hypertranscript");
   const loadingMessageContainer = document.getElementById("hypertranscript");
 
-  const whisperWorkerPath = "./js/whisper.worker.new.js";
-
-  // leave the following three consts as is as they are shared by 
-  // web.worker.js
-
-  const MessageTypes = {
-    DOWNLOADING: "DOWNLOADING",
-    LOADING: "LOADING",
-    RESULT: "RESULT",
-    RESULT_PARTIAL: "RESULT_PARTIAL",
-    INFERENCE_REQUEST: "INFERENCE_REQUEST",
-    INFERENCE_DONE: "INFERENCE_DONE"
-  };
-  
-  const LoadingStatus = {
-    SUCCESS: "success",
-    ERROR: "error",
-    LOADING: "loading"
-  };
-
-  /*const ModelNames = {
-    WHISPER_TINY_EN: "openai/whisper-tiny.en",
-    WHISPER_TINY: "openai/whisper-tiny",
-    WHISPER_BASE: "openai/whisper-base",
-    WHISPER_BASE_EN: "openai/whisper-base.en",
-    WHISPER_SMALL: "openai/whisper-small",
-    WHISPER_SMALL_EN: "openai/whisper-small.en"
-  };*/
+  const whisperWorkerPath = "./js/whisper.worker.js";
 
   let webWorker = createWorker();
-  console.log("webWorker");
-  console.log(webWorker);
-
 
   formSubmitBtn.disabled = true;
   formSubmitBtn.addEventListener("click", async (event2) => {
@@ -90,58 +60,15 @@ function loadWhisperClient(modal) {
 
   function createWorker() {
     const worker = new Worker(whisperWorkerPath, { type: "module" });
-    //const worker = new Worker("./js/whisper.worker.full.js");
-    console.log("worker");
-    console.log(worker);
-    let results = [];
-    worker.onmessage = (event2) => {
-      /*const { type } = event2.data;
-      if (type === MessageTypes.LOADING) {
-        handleLoadingMessage(event2.data);
-      }
-      if (type === MessageTypes.DOWNLOADING) {
-        loadingMessageContainer.innerHTML = '<div class="vertically-centre"><center>Downloading model...</center><br/><img src="'+transcribingSvg+'" width="50" alt="transcribing" style="margin: auto; display: block;"></div>';
-      }
-      if (type === MessageTypes.RESULT) {
-        handleResultMessage(event2.data);
-        results = event2.data.results;
-      }
-      if (type === MessageTypes.RESULT_PARTIAL) {
-        
-      }
-      if (type === MessageTypes.INFERENCE_DONE) {
-        handleInferenceDone(results);
-      }*/
 
-      console.log("whisper wc");
-      console.log(event2.data);
-      handleInferenceDone(event2.data);
+    let results = [];
+    worker.onmessage = (event) => {
+      handleInferenceDone(event.data);
     };
 
     return worker;
   }
 
-  function handleLoadingMessage(data) {
-    const { status } = data;
-
-    if (status === LoadingStatus.SUCCESS) {
-      loadingMessageContainer.innerHTML = '<div class="vertically-centre"><center>Transcribing.... <span id="transcription-progress">0</span>%</center><br/><img src="'+transcribingSvg+'" width="50" alt="transcribing" style="margin: auto; display: block;"></div>';
-    }
-    if (status === LoadingStatus.ERROR) {
-      loadingMessageContainer.innerHTML = '<div class="vertically-centre"><center>Oops! Something went wrong. Please refresh the page and try again.</center><br/><img src="'+errorSvg+'" width="50" alt="error" style="margin: auto; display: block;"></div>';
-    }
-    if (status === LoadingStatus.LOADING) {
-      loadingMessageContainer.innerHTML = '<div class="vertically-centre"><center>Loading model into memory...</center><br/><img src="'+transcribingSvg+'" width="50" alt="transcribing" style="margin: auto; display: block;"></div>';
-    }
-  }
-  
-  function handleResultMessage(data) {
-    const { results, completedUntilTimestamp } = data;
-    const totalDuration = videoPlayer.duration;
-    const progress = completedUntilTimestamp / totalDuration * 100;
-    document.querySelector("#transcription-progress").innerHTML = Math.round(progress);
-  }
-
   function handleInferenceDone(results) {
 
     console.log(results);
@@ -163,18 +90,12 @@ function loadWhisperClient(modal) {
         if (Array.from(word.text)[0].toUpperCase() === Array.from(word.text)[0]){
           wordCapitalised = true;
         }
-        
-        console.log(lastWord);
-        console.log(word.text);
-        console.log(wordCapitalised);
   
         if (wordCapitalised === true && lastWord.endsWith(".") ){
           sentences += 1;
         }
   
         lastWord = word.text;
-  
-        console.log(sentences);
         
         // new para every 5 sentences
         if (sentences % 5 === 0 && sentences !== 0) {
@@ -184,9 +105,8 @@ function loadWhisperClient(modal) {
   
         hypertranscript += `<span data-m='${start}' data-d='${duration}'>${word.text} </span>\n`;
       }
-    
-      //console.log(hypertranscript);
     });
+    
     resultsContainer.innerHTML = "<article>\n <section>\n  <p>\n" + hypertranscript + "  </p>\n </section>\n</article>\n";
 
     const initEvent = new CustomEvent('hyperaudioInit');
@@ -197,21 +117,12 @@ function loadWhisperClient(modal) {
 
   async function handleFormSubmission() {
 
-    console.log("handleFormSubmission()");
-
-    /*if (!isFileUploaded() || !isModelNameSelected()) {
-      return;
-    }*/
-    
-    const model_name = `Xenova/${modelNameSelectionInput.value}`;
+    const model_name = modelNameSelectionInput.value;
     const file = fileUploadBtn.files[0];
     const audio = await readAudioFrom(file);
 
-    console.log("about to postMessage....");
-
-
     webWorker.postMessage({
-      type: MessageTypes.INFERENCE_REQUEST,
+      type: "INFERENCE_REQUEST",
       audio,
       model_name
     });
@@ -231,20 +142,4 @@ function loadWhisperClient(modal) {
     const audio = decoded.getChannelData(0);
     return audio;
   }
-
-  function isFileUploaded() {
-    if (fileUploadBtn.files.length === 0) {
-      return false;
-    }
-    return true;
-  }
-
-  function isModelNameSelected() {
-    const selectedValue = modelNameSelectionInput.value;
-    if (modelNameSelectionInput.value === "") {
-      return false;
-    }
-    const modelName = `Xenova/${selectedValue}`;
-    return Object.values(ModelNames).indexOf(modelName) !== -1;
-  }
 }
\ No newline at end of file
diff --git a/js/modelFactories.js b/js/modelFactories.js
deleted file mode 100644
index a2dbae8..0000000
--- a/js/modelFactories.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import { pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.9.0";
-
-function createModelLoader(model_name) {
-  console.log("in createModelLoader");
-  console.log("attempting to load model:" + model_name);
-  let model = null;
-  const load_model = async ({ progress_callback = undefined }) => {
-    if (model === null) {
-      model = await pipeline("automatic-speech-recognition", model_name, {
-        progress_callback,
-      });
-    }
-    return model;
-  };
-  return load_model;
-}
-
-export { createModelLoader };
\ No newline at end of file
diff --git a/js/util.js b/js/util.js
deleted file mode 100644
index 9cb59ef..0000000
--- a/js/util.js
+++ /dev/null
@@ -1,25 +0,0 @@
-const MessageTypes = {
-  DOWNLOADING: "DOWNLOADING",
-  LOADING: "LOADING",
-  RESULT: "RESULT",
-  RESULT_PARTIAL: "RESULT_PARTIAL",
-  INFERENCE_REQUEST: "INFERENCE_REQUEST",
-  INFERENCE_DONE: "INFERENCE_DONE",
-};
-
-const LoadingStatus = {
-  SUCCESS: "success",
-  ERROR: "error",
-  LOADING: "loading",
-};
-
-const ModelNames = {
-  WHISPER_TINY_EN: "openai/whisper-tiny.en",
-  WHISPER_TINY: "openai/whisper-tiny",
-  WHISPER_BASE: "openai/whisper-base",
-  WHISPER_BASE_EN: "openai/whisper-base.en",
-  WHISPER_SMALL: "openai/whisper-small",
-  WHISPER_SMALL_EN: "openai/whisper-small.en",
-};
-
-export { MessageTypes, ModelNames, LoadingStatus };
\ No newline at end of file
diff --git a/js/whisper.worker.fail.js b/js/whisper.worker.fail.js
deleted file mode 100644
index c1a1f37..0000000
--- a/js/whisper.worker.fail.js
+++ /dev/null
@@ -1,255 +0,0 @@
-import { pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.3.0";
-
-/*import { createModelLoader } from "./modelFactories.js";*/
-/*import { MessageTypes, ModelNames } from "./utils.js";*/
-
-const MessageTypes = {
-  DOWNLOADING: "DOWNLOADING",
-  LOADING: "LOADING",
-  RESULT: "RESULT",
-  RESULT_PARTIAL: "RESULT_PARTIAL",
-  INFERENCE_REQUEST: "INFERENCE_REQUEST",
-  INFERENCE_DONE: "INFERENCE_DONE",
-};
-
-const LoadingStatus = {
-  SUCCESS: "success",
-  ERROR: "error",
-  LOADING: "loading",
-};
-
-const ModelNames = {
-  WHISPER_TINY_EN: "openai/whisper-tiny.en",
-  WHISPER_TINY: "openai/whisper-tiny",
-  WHISPER_BASE: "openai/whisper-base",
-  WHISPER_BASE_EN: "openai/whisper-base.en",
-  WHISPER_SMALL: "openai/whisper-small",
-  WHISPER_SMALL_EN: "openai/whisper-small.en",
-};
-
-console.log("TOP OF THE WORKER");
-
-
-function createModelLoader(model_name) {
-
-  console.log("############# in createModelLoader");
-  
-  let model = null;
-  const load_model = async ({ progress_callback = undefined }) => {
-    if (model === null) {
-      model = await pipeline("automatic-speech-recognition", model_name, {
-        progress_callback,
-      });
-    }
-    return model;
-  };
-  return load_model;
-}
-
-/*const MessageTypes = {
-  DOWNLOADING: "DOWNLOADING",
-  LOADING: "LOADING",
-  RESULT: "RESULT",
-  RESULT_PARTIAL: "RESULT_PARTIAL",
-  INFERENCE_REQUEST: "INFERENCE_REQUEST",
-  INFERENCE_DONE: "INFERENCE_DONE",
-};
-
-const LoadingStatus = {
-  SUCCESS: "success",
-  ERROR: "error",
-  LOADING: "loading",
-};
-
-const ModelNames = {
-  WHISPER_TINY_EN: "openai/whisper-tiny.en",
-  WHISPER_TINY: "openai/whisper-tiny",
-  WHISPER_BASE: "openai/whisper-base",
-  WHISPER_BASE_EN: "openai/whisper-base.en",
-  WHISPER_SMALL: "openai/whisper-small",
-  WHISPER_SMALL_EN: "openai/whisper-small.en",
-};*/
-
-
-const modelLoaders = {};
-for (const model_name of Object.values(ModelNames)) {
-  console.log(model_name);
-  modelLoaders[model_name] = createModelLoader(model_name);
-}
-
-self.addEventListener("message", async (event) => {
-  console.log("RECEIVED MESSAGE");
-  debugger;
-  const { type, audio, model_name } = event.data;
-  if (type === MessageTypes.INFERENCE_REQUEST) {
-    console.log(model_name);
-    await transcribe(audio, model_name);
-    
-  }
-});
-
-async function transcribe(audio, model_name) {
-  // check if model_name is not in modelLoaders
-  sendLoadingMessage("loading", "");
-
-  if (!modelLoaders[model_name]) {
-    console.log("Model not found");
-    sendLoadingMessage("error", "Model not found");
-    return;
-  }
-
-  const pipeline = await modelLoaders[model_name]({
-    callback_function: load_model_callback,
-  });
-  sendLoadingMessage("success");
-
-  const stride_length_s = 5;
-  const generationTracker = new GenerationTracker(pipeline, stride_length_s);
-  await pipeline(audio, {
-    top_k: 0, // TODO: make this configurable via request
-    do_sample: false, // TODO: make this configurable via request
-    chunk_length_s: 30, // TODO: make this configurable via request
-    stride_length_s: stride_length_s, // TODO: make this configurable via request
-    return_timestamps: true,
-    callback_function:
-      generationTracker.callbackFunction.bind(generationTracker),
-    chunk_callback: generationTracker.chunkCallback.bind(generationTracker),
-  });
-  generationTracker.sendFinalResult();
-}
-
-async function load_model_callback(data) {
-  const { status } = data;
-  if (status === "progress") {
-    const { file, progress, loaded, total } = data;
-    sendDownloadingMessage(file, progress, loaded, total);
-  }
-  if (status === "done") {
-    // Do nothing
-  }
-  if (status === "loaded") {
-    // Do nothing
-  }
-}
-
-function sendLoadingMessage(status, message) {
-  self.postMessage({
-    type: MessageTypes.LOADING,
-    status,
-    message,
-  });
-}
-
-function sendDownloadingMessage(file, progress, loaded, total) {
-  self.postMessage({
-    type: MessageTypes.DOWNLOADING,
-    file,
-    progress,
-    loaded,
-    total,
-  });
-}
-
-class GenerationTracker {
-  constructor(pipeline, stride_length_s) {
-    this.pipeline = pipeline;
-    this.stride_length_s = stride_length_s;
-    this.chunks = [];
-    this.time_precision =
-      pipeline.processor.feature_extractor.config.chunk_length /
-      pipeline.model.config.max_source_positions;
-    this.processed_chunks = [];
-    this.callbackFunctionCounter = 0;
-  }
-
-  sendFinalResult() {
-    self.postMessage({ type: MessageTypes.INFERENCE_DONE });
-  }
-
-  callbackFunction(beams) {
-    this.callbackFunctionCounter += 1;
-    if (this.callbackFunctionCounter % 10 !== 0) {
-      return;
-    }
-
-    const bestBeam = beams[0];
-    let text = this.pipeline.tokenizer.decode(bestBeam.output_token_ids, {
-      skip_special_tokens: true,
-    });
-
-    const result = {
-      text,
-      start: this.getLastChuckTimestamp(),
-      end: undefined,
-    };
-    createPartialResultMessage(result);
-  }
-
-  chunkCallback(data) {
-    this.chunks.push(data);
-    const [text, { chunks }] = this.pipeline.tokenizer._decode_asr(
-      this.chunks,
-      {
-        time_precision: this.time_precision,
-        return_timestamps: true,
-        force_full_sequences: false,
-      }
-    );
-    // const newpProcessedChunks = chunks.map(this.processChunk.bind(this));
-    this.processed_chunks = chunks.map((chunk, index) =>
-      this.processChunk(chunk, index)
-    );
-    // this.processed_chunks = this.processed_chunks.concat(newpProcessedChunks);
-    createResultMessage(
-      this.processed_chunks,
-      false,
-      this.getLastChuckTimestamp()
-    );
-  }
-
-  getLastChuckTimestamp() {
-    if (this.processed_chunks.length === 0) {
-      return 0;
-    }
-    return this.processed_chunks[this.processed_chunks.length - 1].end;
-  }
-
-  processChunk(chunk, index) {
-    const { text, timestamp } = chunk;
-    const [start, end] = timestamp;
-
-    return {
-      index,
-      text: `${text.trim()} `,
-      start: Math.round(start),
-      end: Math.round(end) || Math.round(start + 0.9 * this.stride_length_s),
-    };
-  }
-}
-
-function createResultMessage(results, isDone, completedUntilTimestamp) {
-  self.postMessage({
-    type: MessageTypes.RESULT,
-    results,
-    isDone,
-    completedUntilTimestamp,
-  });
-}
-
-function createPartialResultMessage(result) {
-  self.postMessage({
-    type: MessageTypes.RESULT_PARTIAL,
-    result,
-  });
-}
-
-function removeOverlap(s1, s2) {
-  let overlap = Math.min(s1.length, s2.length);
-  while (overlap > 0) {
-    if (s2.startsWith(s1.substring(s1.length - overlap))) {
-      return s2.substring(overlap);
-    }
-    overlap--;
-  }
-  return s2;
-}
diff --git a/js/whisper.worker.new.js b/js/whisper.worker.js
similarity index 71%
rename from js/whisper.worker.new.js
rename to js/whisper.worker.js
index bfc0b81..3aacc8f 100644
--- a/js/whisper.worker.new.js
+++ b/js/whisper.worker.js
@@ -1,20 +1,9 @@
 import { pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.9.0";
 
 self.addEventListener("message", async (event) => {
-
-  
-
   const { type, audio, model_name } = event.data;
 
   if (type === "INFERENCE_REQUEST") {
-    console.log(model_name);
-    console.log(type);
-  
-    /*const automaticSpeechRecognition = await pipeline(
-      "automatic-speech-recognition",
-      "Xenova/whisper-tiny.en",
-      { revision: "output_attentions" }
-    );*/
   
     const automaticSpeechRecognition = await pipeline(
       "automatic-speech-recognition",
@@ -31,7 +20,4 @@ self.addEventListener("message", async (event) => {
     console.log(output);
     self.postMessage({ output: output });
   }
-
-  
-
 });
\ No newline at end of file