[Whisper] Add sequential longform decoding

src/transformers/generation/logits_process.py

@@ -1517,29 +1517,33 @@ class WhisperTimeStampLogitsProcessor(LogitsProcessor):
     ```
     """
 
-    def __init__(self, generate_config):  # support for the kwargs
+    def __init__(
+        self, generate_config, detect_timestamp_from_logprob: Optional[bool] = None
+    ):  # support for the kwargs
         self.eos_token_id = generate_config.eos_token_id
         self.no_timestamps_token_id = generate_config.no_timestamps_token_id
         self.timestamp_begin = generate_config.no_timestamps_token_id + 1
+        self.detect_timestamp_from_logprob = (
+            detect_timestamp_from_logprob
+            if detect_timestamp_from_logprob is not None
+            else getattr(generate_config, "detect_timestamp_from_logprob", True)
+        )
 
-        self.begin_index = len(generate_config.forced_decoder_ids) + 2
-        if generate_config.forced_decoder_ids[-1][1] == self.no_timestamps_token_id:
-            self.begin_index -= 1
-        self.max_initial_timestamp_index = generate_config.max_initial_timestamp_index
+        self.begin_index = (
+            len(generate_config.forced_decoder_ids) + 1 if generate_config.forced_decoder_ids is not None else 1
+        )
+        self.max_initial_timestamp_index = getattr(generate_config, "max_initial_timestamp_index", None)
 
     @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
         # suppress <|notimestamps|> which is handled by without_timestamps
         scores[:, self.no_timestamps_token_id] = -float("inf")
 
-        if input_ids.shape[1] == self.begin_index - 1:
-            scores[:, :] = -float("inf")
-            scores[:, self.timestamp_begin] = 0
-            return scores
-
         # timestamps have to appear in pairs, except directly before eos_token; mask logits accordingly
         for k in range(input_ids.shape[0]):
-            seq = list(input_ids[k, self.begin_index :].tolist())
+            sampled_tokens = input_ids[k, self.begin_index :]
+            seq = list(sampled_tokens.tolist())
+
             last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.timestamp_begin
             penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.timestamp_begin
 
@@ -1549,8 +1553,23 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
                 else:  # cannot be normal text tokens
                     scores[k, : self.eos_token_id] = -float("inf")
 
-            # apply the `max_initial_timestamp` option
-            if input_ids.shape[1] == self.begin_index and self.max_initial_timestamp_index is not None:
+            timestamps = sampled_tokens[sampled_tokens.ge(self.timestamp_begin)]
+            if timestamps.numel() > 0:
+                # `timestamps` shouldn't decrease; forbid timestamp tokens smaller than the last
+                # The following lines of code are copied from: https://github.com/openai/whisper/pull/914/files#r1137085090
+                if last_was_timestamp and not penultimate_was_timestamp:
+                    timestamp_last = timestamps[-1]
+                else:
+                    # Avoid to emit <|0.00|> again
+                    timestamp_last = timestamps[-1] + 1
+
+                scores[k, self.timestamp_begin : timestamp_last] = -float("inf")
+
+        # apply the `max_initial_timestamp` option
+        if input_ids.shape[1] == self.begin_index:
+            scores[:, : self.timestamp_begin] = -float("inf")
+
+            if self.max_initial_timestamp_index is not None:
                 last_allowed = self.timestamp_begin + self.max_initial_timestamp_index
                 scores[:, last_allowed + 1 :] = -float("inf")
 
@@ -1559,7 +1578,7 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         for k in range(input_ids.shape[0]):
             timestamp_logprob = logprobs[k, self.timestamp_begin :].logsumexp(dim=-1)
             max_text_token_logprob = logprobs[k, : self.timestamp_begin].max()
-            if timestamp_logprob > max_text_token_logprob:
+            if timestamp_logprob > max_text_token_logprob and self.detect_timestamp_from_logprob:
                 scores[k, : self.timestamp_begin] = -float("inf")
 
         return scores