Adjusted compute type for tests without gpu.

scraibe/transcriber.py

@@ -32,8 +32,9 @@ from typing import TypeVar, Union, Optional
 from torch import Tensor, device
 from torch.cuda import is_available as cuda_is_available
 from numpy import ndarray
-from inspect import getfullargspec
+from inspect import signature
 from abc import abstractmethod
+import warnings
 
 from .misc import WHISPER_DEFAULT_PATH
 whisper = TypeVar('whisper')
@@ -254,9 +255,7 @@ class WhisperTranscriber(Transcriber):
             dict: Keyword arguments for whisper model.
         """
         # _possible_kwargs = WhisperModel.transcribe.__code__.co_varnames
-        _args = getfullargspec(Whisper.transcribe).args
-        _kwargs = getfullargspec(Whisper.transcribe).kwonlyargs
-        _possible_kwargs = _args + _kwargs
+        _possible_kwargs = signature(Whisper.transcribe).parameters.keys()
 
         whisper_kwargs = {k: v for k,
                           v in kwargs.items() if k in _possible_kwargs}
@@ -343,6 +342,11 @@ class WhisperXTranscriber(Transcriber):
             device = "cuda" if cuda_is_available() else "cpu"
         if not isinstance(device, str):
             device = str(device)
+        compute_type = kwargs.get('compute_type', 'float16')
+        if device == 'cpu' and compute_type == 'float16':
+            warnings.warn(f'Compute type {compute_type} not compatible with '
+                          f'device {device}! Changing compute type to int8.')
+            compute_type = 'int8'
         _model = whisperx_load_model(model, download_root=download_root,
                                      device=device)
 
@@ -357,9 +361,7 @@ class WhisperXTranscriber(Transcriber):
             dict: Keyword arguments for whisper model.
         """
         # _possible_kwargs = WhisperModel.transcribe.__code__.co_varnames
-        _args = getfullargspec(WhisperModel.transcribe).args
-        _kwargs = getfullargspec(WhisperModel.transcribe).kwonlyargs
-        _possible_kwargs = _args + _kwargs
+        _possible_kwargs = signature(WhisperModel.transcribe).parameters.keys()
 
         whisper_kwargs = {k: v for k,
                           v in kwargs.items() if k in _possible_kwargs}