diff --git a/faster_whisper/transcribe.py b/faster_whisper/transcribe.py
index 5da7048..39d25d5 100644
--- a/faster_whisper/transcribe.py
+++ b/faster_whisper/transcribe.py
@@ -28,12 +28,17 @@ class Word(NamedTuple):
 
 
 class Segment(NamedTuple):
+    id: int
+    seek: int
     start: float
     end: float
     text: str
-    words: Optional[List[Word]]
-    avg_log_prob: float
+    tokens: List[int]
+    temperature: float
+    avg_logprob: float
+    compression_ratio: float
     no_speech_prob: float
+    words: Optional[List[Word]]
 
 
 class TranscriptionOptions(NamedTuple):
@@ -335,6 +340,7 @@ class WhisperModel:
         encoder_output: Optional[ctranslate2.StorageView] = None,
     ) -> Iterable[Segment]:
         content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames
+        idx = 0
         seek = 0
         all_tokens = []
         prompt_reset_since = 0
@@ -368,9 +374,12 @@ class WhisperModel:
             if encoder_output is None:
                 encoder_output = self.encode(segment)
 
-            result, avg_log_prob, temperature = self.generate_with_fallback(
-                encoder_output, prompt, tokenizer, options
-            )
+            (
+                result,
+                avg_logprob,
+                temperature,
+                compression_ratio,
+            ) = self.generate_with_fallback(encoder_output, prompt, tokenizer, options)
 
             if options.no_speech_threshold is not None:
                 # no voice activity check
@@ -378,7 +387,7 @@ class WhisperModel:
 
                 if (
                     options.log_prob_threshold is not None
-                    and avg_log_prob > options.log_prob_threshold
+                    and avg_logprob > options.log_prob_threshold
                 ):
                     # don't skip if the logprob is high enough, despite the no_speech_prob
                     should_skip = False
@@ -509,18 +518,24 @@ class WhisperModel:
                     continue
 
                 all_tokens.extend(tokens)
+                idx += 1
 
                 yield Segment(
+                    id=idx,
+                    seek=seek,
                     start=segment["start"],
                     end=segment["end"],
                     text=text,
+                    tokens=tokens,
+                    temperature=temperature,
+                    avg_logprob=avg_logprob,
+                    compression_ratio=compression_ratio,
+                    no_speech_prob=result.no_speech_prob,
                     words=(
                         [Word(**word) for word in segment["words"]]
                         if options.word_timestamps
                         else None
                     ),
-                    avg_log_prob=avg_log_prob,
-                    no_speech_prob=result.no_speech_prob,
                 )
 
     def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
@@ -539,10 +554,11 @@ class WhisperModel:
         prompt: List[int],
         tokenizer: Tokenizer,
         options: TranscriptionOptions,
-    ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float]:
+    ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float, float]:
         result = None
-        avg_log_prob = None
+        avg_logprob = None
         final_temperature = None
+        compression_ratio = None
 
         max_initial_timestamp_index = int(
             round(options.max_initial_timestamp / self.time_precision)
@@ -580,8 +596,8 @@ class WhisperModel:
 
             # Recover the average log prob from the returned score.
             seq_len = len(tokens)
-            cum_log_prob = result.scores[0] * (seq_len**options.length_penalty)
-            avg_log_prob = cum_log_prob / (seq_len + 1)
+            cum_logprob = result.scores[0] * (seq_len**options.length_penalty)
+            avg_logprob = cum_logprob / (seq_len + 1)
 
             text = tokenizer.decode(tokens).strip()
             compression_ratio = get_compression_ratio(text)
@@ -603,21 +619,21 @@ class WhisperModel:
 
             if (
                 options.log_prob_threshold is not None
-                and avg_log_prob < options.log_prob_threshold
+                and avg_logprob < options.log_prob_threshold
             ):
                 needs_fallback = True  # average log probability is too low
 
                 self.logger.debug(
                     "Log probability threshold is not met with temperature %.1f (%f < %f)",
                     temperature,
-                    avg_log_prob,
+                    avg_logprob,
                     options.log_prob_threshold,
                 )
 
             if not needs_fallback:
                 break
 
-        return result, avg_log_prob, final_temperature
+        return result, avg_logprob, final_temperature, compression_ratio
 
     def get_prompt(
         self,