Return result with best log prob when all temperature fallbacks failed (#356)

* Resolve Inference Selection Bug

* Refactor for better readability

* Filter out results with compression_ratio

* Refactor to avoid variable repetition

* Fix incorrect index and perform minor refactoring

* Remove final_temperature variable

faster_whisper/transcribe.py

@@ -578,10 +578,9 @@ class WhisperModel:
         tokenizer: Tokenizer,
         options: TranscriptionOptions,
     ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float, float]:
-        result = None
+        decode_result = None
-        avg_logprob = None
+        all_results = []
-        final_temperature = None
+        below_cr_threshold_results = []
-        compression_ratio = None
 
         max_initial_timestamp_index = int(
             round(options.max_initial_timestamp / self.time_precision)
@@ -601,7 +600,6 @@ class WhisperModel:
                     "patience": options.patience,
                 }
 
-            final_temperature = temperature
             result = self.model.generate(
                 encoder_output,
                 [prompt],
@@ -625,12 +623,18 @@ class WhisperModel:
             text = tokenizer.decode(tokens).strip()
             compression_ratio = get_compression_ratio(text)
 
+            decode_result = (
+                result,
+                avg_logprob,
+                temperature,
+                compression_ratio,
+            )
+            all_results.append(decode_result)
+
             needs_fallback = False
 
-            if (
+            if options.compression_ratio_threshold is not None:
-                options.compression_ratio_threshold is not None
+                if compression_ratio > options.compression_ratio_threshold:
-                and compression_ratio > options.compression_ratio_threshold
-            ):
                     needs_fallback = True  # too repetitive
 
                     self.logger.debug(
@@ -639,6 +643,8 @@ class WhisperModel:
                         compression_ratio,
                         options.compression_ratio_threshold,
                     )
+                else:
+                    below_cr_threshold_results.append(decode_result)
 
             if (
                 options.log_prob_threshold is not None
@@ -661,8 +667,13 @@ class WhisperModel:
 
             if not needs_fallback:
                 break
+        else:
+            # all failed, select the result with the highest average log probability
+            decode_result = max(
+                below_cr_threshold_results or all_results, key=lambda x: x[1]
+            )
 
-        return result, avg_logprob, final_temperature, compression_ratio
+        return decode_result
 
     def get_prompt(
         self,