nocaptions -> nospeech to match the paper figure

whisper/transcribe.py

@@ -23,7 +23,7 @@ def transcribe(
     temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
     compression_ratio_threshold: Optional[float] = 2.4,
     logprob_threshold: Optional[float] = -1.0,
-    no_captions_threshold: Optional[float] = 0.6,
+    no_speech_threshold: Optional[float] = 0.6,
     **decode_options,
 ):
     """
@@ -50,8 +50,8 @@ def transcribe(
     logprob_threshold: float
         If the average log probability over sampled tokens is below this value, treat as failed
 
-    no_captions_threshold: float
-        If the no_captions probability is higher than this value AND the average log probability
+    no_speech_threshold: float
+        If the no_speech probability is higher than this value AND the average log probability
         over sampled tokens is below `logprob_threshold`, consider the segment as silent
 
     decode_options: dict
@@ -148,7 +148,7 @@ def transcribe(
                 "temperature": result.temperature,
                 "avg_logprob": result.avg_logprob,
                 "compression_ratio": result.compression_ratio,
-                "no_caption_prob": result.no_caption_prob,
+                "no_speech_prob": result.no_speech_prob,
             }
         )
         if verbose:
@@ -163,11 +163,11 @@ def transcribe(
         result = decode_with_fallback(segment)[0]
         tokens = torch.tensor(result.tokens)
 
-        if no_captions_threshold is not None:
+        if no_speech_threshold is not None:
             # no voice activity check
-            should_skip = result.no_caption_prob > no_captions_threshold
+            should_skip = result.no_speech_prob > no_speech_threshold
             if logprob_threshold is not None and result.avg_logprob > logprob_threshold:
-                # don't skip if the logprob is high enough, despite the no_captions_prob
+                # don't skip if the logprob is high enough, despite the no_speech_prob
                 should_skip = False
 
             if should_skip:
@@ -249,7 +249,7 @@ def cli():
     parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
     parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
     parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
-    parser.add_argument("--no_caption_threshold", type=optional_float, default=0.6, help="if the probability of the <|nocaptions|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
+    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
 
     args = parser.parse_args().__dict__
     model_name: str = args.pop("model")
@@ -261,12 +261,8 @@ def cli():
         warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
         args["language"] = "en"
 
-    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
-    compression_ratio_threshold = args.pop("compression_ratio_threshold")
-    logprob_threshold = args.pop("logprob_threshold")
-    no_caption_threshold = args.pop("no_caption_threshold")
-
     temperature = args.pop("temperature")
+    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
     if temperature_increment_on_fallback is not None:
         temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
     else:
@@ -276,15 +272,7 @@ def cli():
     model = load_model(model_name, device=device)
 
     for audio_path in args.pop("audio"):
-        result = transcribe(
-            model,
-            audio_path,
-            temperature=temperature,
-            compression_ratio_threshold=compression_ratio_threshold,
-            logprob_threshold=logprob_threshold,
-            no_captions_threshold=no_caption_threshold,
-            **args,
-        )
+        result = transcribe(model, audio_path, temperature=temperature, **args)
 
         audio_basename = os.path.basename(audio_path)