0.2.10

available_models.md

@@ -1,4 +1,4 @@
-# Available model sizes:
+# Available Whisper model sizes:
 
 - tiny.en (english only)
 - tiny
@@ -71,3 +71,39 @@
 3. Good hardware and want best quality? → `large-v3`
 4. Need fast, high-quality transcription without translation? → `large-v3-turbo`
 5. Need translation capabilities? → `large-v2` or `large-v3` (avoid turbo)
+
+
+_______________________
+
+# Translation Models and Backend
+
+**Language Support**: ~200 languages
+
+## Distilled Model Sizes Available
+
+| Model | Size | Parameters | VRAM (FP16) | VRAM (INT8) | Quality |
+|-------|------|------------|-------------|-------------|---------|
+| 600M | 2.46 GB | 600M | ~1.5GB | ~800MB | Good, understandable |
+| 1.3B | 5.48 GB | 1.3B | ~3GB | ~1.5GB | Better accuracy, context |
+
+**Quality Impact**: 1.3B has ~15-25% better BLEU scores vs 600M across language pairs.
+
+## Backend Performance
+
+| Backend | Speed vs Base | Memory Usage | Quality Loss |
+|---------|---------------|--------------|--------------|
+| CTranslate2 | 6-10x faster | 40-60% less | ~5% BLEU drop |
+| Transformers | Baseline | High | None |
+| Transformers + MPS (on Apple Silicon) | 2x faster | Medium | None |
+
+**Metrics**:
+- CTranslate2: 50-100+ tokens/sec
+- Transformers: 10-30 tokens/sec
+- Apple Silicon with MPS: Up to 2x faster than CTranslate2
+
+## Quick Decision Matrix
+
+**Choose 600M**: Limited resources, close to 0 lag
+**Choose 1.3B**: Quality matters
+**Choose Transformers**: On Apple Silicon
+

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "whisperlivekit"
-version = "0.2.9"
+version = "0.2.10"
 description = "Real-time speech-to-text with speaker diarization using Whisper"
 readme = "README.md"
 authors = [

whisperlivekit/audio_processor.py

@@ -445,8 +445,8 @@ class AudioProcessor:
                 elif not lines:
                     lines = [Line(
                         speaker=1,
-                        start=state.get("end_buffer", 0),
+                        start=state.end_buffer,
-                        end=state.get("end_buffer", 0)        
+                        end=state.end_buffer
                     )]
                 
                 response = FrontData(

whisperlivekit/web/live_transcription.js

@@ -346,13 +346,6 @@ function renderLinesWithBuffer(
 
       let currentLineText = item.text || "";
 
-      if (item.translation) {
-        currentLineText += `<div class="label_translation">
-          <img src="/web/src/translate.svg" alt="Translation" width="12" height="12" />
-          <span>${item.translation}</span>
-        </div>`;
-      }
-
       if (idx === lines.length - 1) {
         if (!isFinalizing && item.speaker !== -2) {
           if (remaining_time_transcription > 0) {
@@ -386,6 +379,13 @@ function renderLinesWithBuffer(
         }
       }
       
+      if (item.translation) {
+        currentLineText += `<div class="label_translation">
+          <img src="/web/src/translate.svg" alt="Translation" width="12" height="12" />
+          <span>${item.translation}</span>
+        </div>`;
+      }
+
       return currentLineText.trim().length > 0 || speakerLabel.length > 0
         ? `<p>${speakerLabel}<br/><div class='textcontent'>${currentLineText}</div></p>`
         : `<p>${speakerLabel}<br/></p>`;