voice recognition kinda works??

app/backend/InputOutputHandler.tsx

@@ -167,16 +167,14 @@ const InputOutputBackend: React.FC = () => {
         
     mediaRecorder.onstop = async () => {
       const audioBlob = new Blob(audioChunks.current, { type: "audio/ogg" })
-      console.log(audioBlob);
-      const url = URL.createObjectURL(audioBlob)
-      const audio = new Audio(url);
-      audio.play().catch(error => console.error("Error playing audio:", error));
-      console.log(url);
-      setAudioURL(url)
       audioChunks.current = []
-      const wavBlob = await convertOggToWav(audioBlob)
+      // console.log(audioBlob);
+      // const url = URL.createObjectURL(audioBlob)
+      // const audio = new Audio(url);
+      // audio.play().catch(error => console.error("Error playing audio:", error));
+
       const remote = new VoiceSend()
-      remote.sendToVoiceRecognition(wavBlob)
+      remote.sendToVoiceRecognition(audioBlob)
     }
 
     mediaRecorder.start()
@@ -276,14 +274,9 @@ const InputOutputBackend: React.FC = () => {
         onMicClick={handleMicClick}
         inputDisabled={inputDisabled}
         isRecording={isRecording}
-      />
+      /> 
     </div>
   )
 }
 
-export default InputOutputBackend
+export default InputOutputBackend
-
-
-
-
-

py/api.py

@@ -1,4 +1,4 @@
-from flask i                mport Flask, request, jsonify
+from flask import Flask, request, jsonify
 from flask_cors import CORS
 import secrets
 import threading

py/voice.py

@@ -1,26 +1,32 @@
 import io
 import numpy as np
-import whisper
+from faster_whisper import WhisperModel
 from pydub import AudioSegment
 
 
 class VoiceRecognition:
     @staticmethod
     def recognition(audio):
-        audio_data = audio.read()
+        audio_buffer = io.BytesIO(audio.read())
-        with io.BytesIO(audio_data) as audio_buffer:
-            audio_segment = AudioSegment.from_ogg(audio_buffer)
-            
-            raw_data = np.array(audio_segment.get_array_of_samples())
-            
-            if audio_segment.channels > 1:
-                raw_data = raw_data.reshape((-1, audio_segment.channels))[:, 0]
-            
-            audio_data = raw_data.astype(np.float32) / (2 ** (audio_segment.sample_width * 8 - 1))
         
-        model = whisper.load_model("base")
+        try:
-        result = model.transcribe(audio_data)
+            audio_segment = AudioSegment.from_file(audio_buffer, format="ogg")
-        print(result["text"])
+            
-        return result["text"]
+            wav_io = io.BytesIO()
+            audio_segment.export(wav_io, format="wav")
+            wav_io.seek(0)
+        except:
+            print("audio to wav failed")
+        
+        model_size = "base"
+        model = WhisperModel(model_size, device="cpu", compute_type="int8")
+        
+        segments, _ = model.transcribe(wav_io)
+        transcription = ""
+        for segment in segments:
+            transcription += segment.text + " "
+        result = transcription.strip()
+        print(result)
+        return result
 
 # npm install @ffmpeg/ffmpeg @ffmpeg/util @ffmpeg/types @ffmpeg/core-mt