fix(batch): add temperature fallback to prevent decoder runaway

Signed-off-by: makaveli10 <vineet.suryan@collabora.com>

whisper_live/backend/faster_whisper_backend.py

@@ -220,6 +220,7 @@ class ServeClientFasterWhisper(ServeClientBase):
                 use_vad=self.use_vad,
                 vad_parameters=self.vad_parameters if self.use_vad else None,
                 word_timestamps=self.word_timestamps,
+                client_uid=self.client_uid,
             )
             ServeClientFasterWhisper.BATCH_WORKER.submit(request)
             request.future.wait(timeout=30)

whisper_live/batch_inference.py

@@ -74,6 +74,8 @@ class BatchRequest:
     initial_prompt: Optional[str] = None
     use_vad: bool = True
     vad_parameters: Optional[Dict] = None
+    word_timestamps: bool = False
+    client_uid: Optional[str] = None
     # Signaling
     future: threading.Event = field(default_factory=threading.Event)
     # Results (filled by batch worker)
@@ -307,13 +309,38 @@ class BatchInferenceWorker:
                 tokenizers_list.append(tokenizer)
                 prompts.append(prompt)
 
-            # Step 4: Batch GPU generate
+            # Step 4: Batch GPU generate with per-item temperature fallback.
+            # Mirrors faster_whisper.transcribe()'s fallback loop. Items that
+            # pass quality thresholds at lower temperature keep their result;
+            # only failed items are re-decoded at the next temperature.
             suppress_tokens = get_suppressed_tokens(tokenizers_list[0], [-1])
 
-            results = self.transcriber.model.generate(
+            temperatures = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
-                encoder_output,
+            comp_thresh = 2.4
-                prompts,
+            logprob_thresh = -1.0
-                beam_size=5,
+            no_speech_thresh = 0.6
+
+            n = len(preprocessed)
+            final_results = [None] * n  # tuples of (gen_result, avg_logprob, used_temp)
+            pending_indices = list(range(n))
+
+            for temp in temperatures:
+                if not pending_indices:
+                    break
+
+                if len(pending_indices) == n:
+                    sub_encoder = encoder_output
+                else:
+                    # Re-encode features for just the pending items to get
+                    # an encoder_output of the right batch dimension.
+                    sub_feature_batch = np.stack(
+                        [preprocessed[i][1] for i in pending_indices]
+                    )
+                    sub_encoder = self.transcriber.encode(sub_feature_batch)
+                sub_prompts = [prompts[i] for i in pending_indices]
+
+                gen_kwargs = dict(
+                    beam_size=5 if temp == 0.0 else 1,
                     patience=1,
                     length_penalty=1,
                     max_length=self.transcriber.max_length,
@@ -321,22 +348,48 @@ class BatchInferenceWorker:
                     suppress_tokens=suppress_tokens,
                     return_scores=True,
                     return_no_speech_prob=True,
-                sampling_temperature=0.0,
+                    sampling_temperature=temp,
                     repetition_penalty=1,
                     no_repeat_ngram_size=0,
                 )
+                batch_results = self.transcriber.model.generate(
+                    sub_encoder, sub_prompts, **gen_kwargs
+                )
+
+                next_pending = []
+                for j, idx in enumerate(pending_indices):
+                    gen_result = batch_results[j]
+                    tokens = gen_result.sequences_ids[0]
+                    seq_len = len(tokens)
+                    cum_logprob = gen_result.scores[0] * seq_len
+                    avg_logprob = cum_logprob / (seq_len + 1) if seq_len > 0 else 0.0
+                    raw_text = tokenizers_list[idx].decode(tokens).strip()
+                    comp_ratio = get_compression_ratio(raw_text) if raw_text else 0.0
+
+                    bad = (
+                        comp_ratio > comp_thresh
+                        or avg_logprob < logprob_thresh
+                    )
+                    # High no_speech + low logprob -> treat as silence, accept empty.
+                    is_silence = (
+                        gen_result.no_speech_prob > no_speech_thresh
+                        and avg_logprob < logprob_thresh
+                    )
+
+                    if not bad or is_silence or temp == temperatures[-1]:
+                        final_results[idx] = (gen_result, avg_logprob, temp)
+                    else:
+                        next_pending.append(idx)
+
+                pending_indices = next_pending
 
             # Step 5: Per-item segment parsing and result dispatch
             for i, (req, features, audio, duration, speech_chunks) in enumerate(preprocessed):
                 try:
                     tokenizer = tokenizers_list[i]
-                    gen_result = results[i]
+                    gen_result, avg_logprob, used_temp = final_results[i]
 
                     tokens = gen_result.sequences_ids[0]
-                    seq_len = len(tokens)
-                    cum_logprob = gen_result.scores[0] * seq_len
-                    avg_logprob = cum_logprob / (seq_len + 1) if seq_len > 0 else 0.0
-
                     segment_size = int(ceil(duration) * self.transcriber.frames_per_second)
 
                     subsegments, _, _ = self.transcriber._split_segments_by_timestamps(
@@ -364,7 +417,7 @@ class BatchInferenceWorker:
                             compression_ratio=get_compression_ratio(text),
                             no_speech_prob=gen_result.no_speech_prob,
                             words=None,
-                            temperature=0.0,
+                            temperature=used_temp,
                         ))
 
                     req.result = segments