Add translation functionality to transcribed text

---

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Audio-Transcription-Chrome/background.js

@@ -159,6 +159,7 @@ async function startCapture(options) {
           task: options.task,
           modelSize: options.modelSize,
           useVad: options.useVad,
+          targetLanguage: options.targetLanguage, // Added target language for translation
         },
       });
     } else {
@@ -188,6 +189,25 @@ async function stopCapture() {
 }
 
 
+/**
+ * Sends transcribed text to a large model for translation and sends the translated text to the client.
+ * @param {string} text - The transcribed text to be translated.
+ * @param {string} targetLanguage - The target language for translation.
+ * @returns {Promise<string>} - A Promise that resolves to the translated text.
+ */
+async function translateText(text, targetLanguage) {
+  // Placeholder function for sending transcribed text to a large model for translation
+  // Implement the actual translation logic here
+  return new Promise((resolve) => {
+    // Simulate translation delay
+    setTimeout(() => {
+      const translatedText = `Translated (${targetLanguage}): ${text}`;
+      resolve(translatedText);
+    }, 1000);
+  });
+}
+
+
 /**
  * Listens for messages from the runtime and performs corresponding actions.
  * @param {Object} message - The message received from the runtime.
@@ -205,6 +225,10 @@ chrome.runtime.onMessage.addListener(async (message) => {
     chrome.runtime.sendMessage({ action: "toggleCaptureButtons", data: false });
     chrome.storage.local.set({ capturingState: { isCapturing: false } })
     stopCapture();
+  } else if (message.action === "translateText") {
+    const { text, targetLanguage } = message;
+    const translatedText = await translateText(text, targetLanguage);
+    chrome.runtime.sendMessage({ action: "translatedText", translatedText });
   }
 });
 

Audio-Transcription-Chrome/options.js

@@ -100,7 +100,8 @@ async function startRecord(option) {
           language: option.language,
           task: option.task,
           model: option.modelSize,
-          use_vad: option.useVad
+          use_vad: option.useVad,
+          targetLanguage: option.targetLanguage, // Added target language for translation
         })
       );
     };

whisper_live/transcriber.py

@@ -474,7 +474,6 @@ def generate_segments(
         # NOTE: This loop is obscurely flattened to make the diff readable.
         # A later commit should turn this into a simpler nested loop.
         # for seek_clip_start, seek_clip_end in seek_clips:
-        #     while seek < seek_clip_end
         while clip_idx < len(seek_clips):
             seek_clip_start, seek_clip_end = seek_clips[clip_idx]
             if seek_clip_end > content_frames:
@@ -1025,7 +1024,7 @@ def add_word_timestamps(
                     and segment["start"] - 0.5 > words[0]["start"]
                 ):
                     words[0]["start"] = max(
-                        0, min(words[0]["end"] - median_duration, segment["start"])
+                        0, min(words[0]["end"] - max_duration, segment["start"])
                     )
                 else:
                     segment["start"] = words[0]["start"]
@@ -1041,8 +1040,6 @@ def add_word_timestamps(
                 else:
                     segment["end"] = words[-1]["end"]
 
-                last_speech_timestamp = segment["end"]
-
             segment["words"] = words
 
     def find_alignment(
@@ -1102,6 +1099,22 @@ def find_alignment(
             )
         ]
 
+    def translate(
+        self,
+        text: str,
+        target_language: str,
+        model: str = "Helsinki-NLP/opus-mt-en-ROMANCE",
+    ) -> str:
+        from transformers import MarianMTModel, MarianTokenizer
+
+        tokenizer = MarianTokenizer.from_pretrained(model)
+        model = MarianMTModel.from_pretrained(model)
+
+        translated = model.generate(**tokenizer(text, return_tensors="pt", padding=True))
+        translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+
+        return translated_text
+
 
 def restore_speech_timestamps(
     segments: Iterable[Segment],

whisper_live/transcriber_tensorrt.py

@@ -312,6 +312,11 @@ def transcribe(
         prediction = re.sub(r'<\|.*?\|>', '', prediction)
         return prediction.strip()
 
+    def translate(self, text, target_language):
+        # Placeholder function for sending transcribed text to a large model for translation
+        # Implement the actual translation logic here
+        return f"Translated ({target_language}): {text}"
+
 
 def decode_wav_file(
         model,