add stt method to the transcribe function

whisper/transcribe.py

@@ -17,7 +17,7 @@ from .audio import (
     log_mel_spectrogram,
     pad_or_trim,
 )
-from .decoding import DecodingOptions, DecodingResult
+from .decoding import DecodingOptions, DecodingResult, decode
 from .timing import add_word_timestamps
 from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
 from .utils import (
@@ -498,6 +498,40 @@ def transcribe(
     )
 
 
+def stt(model: "Whisper", 
+        audio: Union[str, np.ndarray, torch.Tensor],
+        language : str = "en", 
+        f16: bool =True):
+    """
+    Transcribe an audio file using Whisper while getting the probability for each token
+
+    Parameters
+    ----------
+    model: Whisper
+        The Whisper model instance
+
+    audio: Union[str, np.ndarray, torch.Tensor]
+        The path to the audio file to open, or the audio waveform
+    
+    language: string 
+        language used in the audio
+
+    f16: bool
+        check if using torch.float16 otherwise use torch.float32
+    """
+    dtype = torch.float16 if f16 else torch.float32
+    audio = pad_or_trim(audio)
+    mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES).to(model.device)
+    mel_segment =pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
+
+    options = DecodingOptions()
+    result = decode(model, mel_segment, options)
+    tokenizer = get_tokenizer(multilingual=model.is_multilingual, language=language, task=options.task)
+    text = [tokenizer.decode([t]) for t in result.tokens]
+    output = [ [text, prob] for text, prob in zip(text, result.token_probs) ]
+    return output
+
+
 def cli():
     from . import available_models