Portability bugfixes

* Expose option to run transcription engine on CPU instead of GPU
* Use embedded git when setting up the Python virtual environment

1 files changed, 18 insertions, 10 deletions

diff --git a/Scripts/transcribe.py b/Scripts/transcribe.py
index 8491e4d..4d36e53 100644
--- a/Scripts/transcribe.py
+++ b/Scripts/transcribe.py
@@ -202,8 +202,7 @@ def resetAudio(audio_state):
     audio_state.transcribe_lock.release()
 
 # Transcribe the audio recorded in a file.
-def transcribe(audio_state, model, frames):
-
+def transcribe(audio_state, model, frames, use_cpu: bool):
     start_time = time.time()
 
     frames = audio_state.frames
@@ -223,8 +222,10 @@ def transcribe(audio_state, model, frames):
     #for temp in (0.00, 0.05, 0.10, 0.15, 0.20):
     #for temp in (0.00, 0.05):
     for temp in (0.00,):
+        use_gpu = not use_cpu
         options = whisper.DecodingOptions(language = audio_state.language,
-                beam_size = 5, temperature = temp, without_timestamps = True)
+                beam_size = 5, temperature = temp, without_timestamps = True,
+                fp16 = use_gpu)
         result = whisper.decode(model, mel, options)
 
         if result.avg_logprob < -1.0:
@@ -247,7 +248,7 @@ def transcribe(audio_state, model, frames):
 
     return result
 
-def transcribeAudio(audio_state, model):
+def transcribeAudio(audio_state, model, use_cpu: bool):
     last_transcribe_time = time.time()
     while audio_state.run_app == True:
         # Pace this out
@@ -266,7 +267,7 @@ def transcribeAudio(audio_state, model):
                 audio_state.transcribe_sleep_duration_max_s,
                 longer_sleep_dur)
 
-        text = transcribe(audio_state, model, audio_state.frames)
+        text = transcribe(audio_state, model, audio_state.frames, use_cpu)
         if not text:
             print("no transcription, spin ({} seconds)".format(time.time() - last_transcribe_time))
             last_transcribe_time = time.time()
@@ -373,7 +374,7 @@ def readControllerInput(audio_state, enable_local_beep):
 
 # model should correspond to one of the Whisper models defined in
 # whisper/__init__.py. Examples: tiny, base, small, medium.
-def transcribeLoop(mic: str, language: str, model: str, enable_local_beep: bool):
+def transcribeLoop(mic: str, language: str, model: str, enable_local_beep: bool, use_cpu: bool):
     audio_state = getMicStream(mic)
     audio_state.language = whisper.tokenizer.TO_LANGUAGE_CODE[language]
 
@@ -386,7 +387,7 @@ def transcribeLoop(mic: str, language: str, model: str, enable_local_beep: bool)
     print("Model {} will be saved to {}".format(model, model_root))
     model = whisper.load_model(model, download_root=model_root)
 
-    transcribe_audio_thd = threading.Thread(target = transcribeAudio, args = [audio_state, model])
+    transcribe_audio_thd = threading.Thread(target = transcribeAudio, args = [audio_state, model, use_cpu])
     transcribe_audio_thd.daemon = True
     transcribe_audio_thd.start()
 
@@ -432,10 +433,11 @@ if __name__ == "__main__":
     parser.add_argument("--model", type=str, help="Which AI model to use. Ex: tiny, base, small, medium")
     parser.add_argument("--bytes_per_char", type=str, help="The number of bytes to use to represent each character")
     parser.add_argument("--chars_per_sync", type=str, help="The number of characters to send on each sync event")
-    parser.add_argument("--enable_local_beep", type=int, help="Whether to play a local auditory indicator when transcription starts/stops.");
+    parser.add_argument("--enable_local_beep", type=int, help="Whether to play a local auditory indicator when transcription starts/stops.")
     parser.add_argument("--rows", type=int, help="The number of rows on the board")
     parser.add_argument("--cols", type=int, help="The number of columns on the board")
-    parser.add_argument("--window_duration_s", type=int, help="The length in seconds of the audio recording handed to the transcription algorithm");
+    parser.add_argument("--window_duration_s", type=int, help="The length in seconds of the audio recording handed to the transcription algorithm")
+    parser.add_argument("--cpu", type=int, help="If set to 1, use CPU instead of GPU")
     args = parser.parse_args()
 
     if not args.mic:
@@ -458,10 +460,16 @@ if __name__ == "__main__":
     if args.window_duration_s:
         config.MAX_LENGTH_S = int(args.window_duration_s)
 
+    if args.cpu == 1:
+        args.cpu = True
+    else:
+        args.cpu = False
+
     generate_utils.config.BYTES_PER_CHAR = int(args.bytes_per_char)
     generate_utils.config.CHARS_PER_SYNC = int(args.chars_per_sync)
     generate_utils.config.BOARD_ROWS = int(args.rows)
     generate_utils.config.BOARD_COLS = int(args.cols)
 
-    transcribeLoop(args.mic, args.language, args.model, args.enable_local_beep)
+    transcribeLoop(args.mic, args.language, args.model, args.enable_local_beep,
+            args.cpu)