Merge branch 'main' into add-mac-os-support

AudioTranscriber.py

@@ -50,8 +50,14 @@ class AudioTranscriber:
             who_spoke, data, time_spoken = audio_queue.get()
             self.update_last_sample_and_phrase_status(who_spoke, data, time_spoken)
             source_info = self.audio_sources[who_spoke]
-            temp_file = source_info["process_data_func"](source_info["last_sample"])
-            text = self.audio_model.get_transcription(temp_file)
+
+            text = ''
+            temp_file = NamedTemporaryFile(delete=False, suffix=".wav")
+            source_info["process_data_func"](source_info["last_sample"], temp_file.name)
+            text = self.audio_model.get_transcription(temp_file.name)
+
+            temp_file.close()
+            os.unlink(temp_file.name)
 
             if text != '' and text.lower() != 'you':
                 self.update_transcript(who_spoke, text, time_spoken)
@@ -68,23 +74,19 @@ class AudioTranscriber:
         source_info["last_sample"] += data
         source_info["last_spoken"] = time_spoken 
 
-    def process_mic_data(self, data):
-        temp_file = NamedTemporaryFile().name
+    def process_mic_data(self, data, temp_file_name):
         audio_data = sr.AudioData(data, self.audio_sources["You"]["sample_rate"], self.audio_sources["You"]["sample_width"])
         wav_data = io.BytesIO(audio_data.get_wav_data())
-        with open(temp_file, 'w+b') as f:
+        with open(temp_file_name, 'w+b') as f:
             f.write(wav_data.read())
-        return temp_file
 
-    def process_speaker_data(self, data):
-        temp_file = NamedTemporaryFile().name
-        with wave.open(temp_file, 'wb') as wf:
+    def process_speaker_data(self, data, temp_file_name):
+        with wave.open(temp_file_name, 'wb') as wf:
             wf.setnchannels(self.audio_sources["Speaker"]["channels"])
             p = pyaudio.PyAudio()
             wf.setsampwidth(p.get_sample_size(pyaudio.paInt16))
             wf.setframerate(self.audio_sources["Speaker"]["sample_rate"])
             wf.writeframes(data)
-        return temp_file
 
     def update_transcript(self, who_spoke, text, time_spoken):
         source_info = self.audio_sources[who_spoke]

README.md

@@ -15,7 +15,7 @@ Follow these steps to set up and run Ecoute on your local machine.
 
 ### 📋 Prerequisites
 
-- Python 3.x
+- Python >=3.8.0
 - An OpenAI API key
 - Windows OS (Not tested on others)
 - FFmpeg 
@@ -73,11 +73,18 @@ If FFmpeg is not installed in your system, you can follow the steps below to ins
    
 4. Create a `keys.py` file in the ecoute directory and add your OpenAI API key:
 
-   ```
-   python -c "with open('keys.py', 'w', encoding='utf-8') as f: f.write('OPENAI_API_KEY=\"API KEY\"')"
-   ```
+   - Option 1: You can utilize a command on your command prompt. Run the following command, ensuring to replace "API KEY" with your actual OpenAI API key:
 
-   Replace `API KEY` with your actual OpenAI API key.
+      ```
+      python -c "with open('keys.py', 'w', encoding='utf-8') as f: f.write('OPENAI_API_KEY=\"API KEY\"')"
+      ```
+
+   - Option 2: You can create the keys.py file manually. Open up your text editor of choice and enter the following content:
+   
+      ```
+      OPENAI_API_KEY="API KEY"
+      ```
+      Replace "API KEY" with your actual OpenAI API key. Save this file as keys.py within the ecoute directory.
 
 
 ### 🎬 Running Ecoute

TranscriberModels.py

@@ -24,9 +24,7 @@ class WhisperTranscriber:
     
 class APIWhisperTranscriber:
     def get_transcription(self, wav_file_path):
-        new_file_path = wav_file_path + '.wav'
-        os.rename(wav_file_path, new_file_path)
-        audio_file= open(new_file_path, "rb")
+        audio_file= open(wav_file_path, "rb")
         try:
             result = openai.Audio.translate("whisper-1", audio_file)
         except Exception as e: