diff --git a/granite/run_eval_speculative.py b/granite/run_eval_speculative.py
new file mode 100644
index 0000000..54325a7
--- /dev/null
+++ b/granite/run_eval_speculative.py
@@ -0,0 +1,289 @@
+"""
+Self-speculative decoding for Speech LLMs.
+"""
+
+import argparse
+import math
+import os
+import torch
+import torch.nn.functional as F
+import evaluate
+from normalizer import data_utils
+import time
+from tqdm import tqdm
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, models
+
+assert hasattr(models, "granite_speech")
+
+wer_metric = evaluate.load("wer")
+torch.set_float32_matmul_precision('high')
+
+
+def main(args):
+    device = f"cuda:{args.device}" if args.device >= 0 else "cpu"
+
+    processor = AutoProcessor.from_pretrained(args.model_id)
+    tokenizer = processor.tokenizer
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(args.model_id, torch_dtype=torch.bfloat16).to(device)
+    model.eval()
+
+    logits_scaling = getattr(model.language_model.config, 'logits_scaling', 1.0)
+
+    # ========== Chat Template Setup ==========
+    text_instruction = "<|audio|>can you transcribe the speech into a written format?"
+
+    # Build chat message and apply template
+    message = [
+        {"role": "user", "content": text_instruction},
+    ]
+    text_prompt = tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
+
+    # Derive prefix and suffix from the formatted prompt
+    prompt_prefix, prompt_suffix = text_prompt.split("<|audio|>")
+
+    # Cache prompt embeddings (suffix excludes <|audio|> since we insert audio embeds separately)
+    embed_layer = model.language_model.get_input_embeddings()
+    prefix_ids = tokenizer.encode(prompt_prefix, add_special_tokens=False)
+    suffix_ids = tokenizer.encode(prompt_suffix, add_special_tokens=False)
+    cached_prefix_embeds = embed_layer(torch.tensor([prefix_ids], device=device))
+    cached_suffix_embeds = embed_layer(torch.tensor([suffix_ids], device=device))
+
+    HOP_LENGTH = 160
+    confidence_threshold = args.confidence_threshold
+    ctc_threshold = args.ctc_threshold
+
+    @torch.no_grad()
+    def ctc_decode(audios):
+        """CTC decode with entropy-based confidence."""
+        texts = [text_prompt] * len(audios)
+        model_inputs = processor(texts, audios, device=device, return_tensors="pt").to(device)
+
+        with torch.amp.autocast(device_type='cuda', dtype=torch.bfloat16):
+            encoder_output = model.encoder(model_inputs["input_features"])
+            embeddings = encoder_output.last_hidden_state if hasattr(encoder_output, 'last_hidden_state') else encoder_output
+            ctc_logits = model.encoder.out(embeddings)
+            ctc_probs = F.softmax(ctc_logits.float(), dim=-1)
+
+        _, idx_batch = ctc_probs.max(dim=-1)
+        entropy = -(ctc_probs * torch.log(ctc_probs + 1e-10)).sum(dim=-1)
+
+        ctc_texts, ctc_entropies, embed_lengths = [], [], []
+        for i, idx in enumerate(idx_batch):
+            dedup = torch.unique_consecutive(idx, dim=-1)
+            non_blank = dedup[dedup > 0].tolist()
+            ctc_texts.append(''.join(chr(c) for c in non_blank))
+            ctc_entropies.append(entropy[i].max().item() if non_blank else float('inf'))
+            embed_lengths.append(len(audios[i]) // HOP_LENGTH // 2 + 1)
+
+        return ctc_texts, ctc_entropies, embeddings, embed_lengths
+
+    @torch.no_grad()
+    def verify(ctc_texts, embeddings, embed_lengths):
+        """Verify CTC outputs with LLM."""
+        batch_sz = len(ctc_texts)
+
+        ctc_token_ids = []
+        for text in ctc_texts:
+            text = text.strip() if text else ""
+            ctc_token_ids.append(tokenizer.encode(text, add_special_tokens=False) if text else [])
+
+        with torch.amp.autocast(device_type='cuda', dtype=torch.bfloat16):
+            audio_embeds = model.projector(embeddings)
+        max_proj_len = audio_embeds.shape[1]
+
+        window_size, downsample_rate = model.config.window_size, model.config.downsample_rate
+        num_queries = window_size // downsample_rate
+        proj_lengths = [min(math.ceil(enc_len / window_size) * num_queries, max_proj_len) for enc_len in embed_lengths]
+
+        if not any(ctc_token_ids):
+            return [(False, ctc_texts[i]) for i in range(batch_sz)], audio_embeds, proj_lengths
+
+        audio_token_id = model.config.audio_token_id
+        all_input_ids, prompt_lens, audio_ranges = [], [], []
+
+        for i, proj_len in enumerate(proj_lengths):
+            audio_start = len(prefix_ids)
+            audio_ranges.append((audio_start, audio_start + proj_len))
+            prompt_part = prefix_ids + [audio_token_id] * proj_len + suffix_ids
+            prompt_lens.append(len(prompt_part))
+            all_input_ids.append(prompt_part + ctc_token_ids[i])
+
+        max_len = max(len(ids) for ids in all_input_ids)
+        padded_ids = torch.full((batch_sz, max_len), tokenizer.pad_token_id, dtype=torch.long, device=device)
+        attn_mask = torch.zeros(batch_sz, max_len, dtype=torch.long, device=device)
+        for i, ids in enumerate(all_input_ids):
+            padded_ids[i, :len(ids)] = torch.tensor(ids, dtype=torch.long, device=device)
+            attn_mask[i, :len(ids)] = 1
+
+        inputs_embeds = model.language_model.get_input_embeddings()(padded_ids)
+        for i in range(batch_sz):
+            s, e = audio_ranges[i]
+            inputs_embeds[i, s:e, :] = audio_embeds[i, :e-s, :]
+
+        with torch.amp.autocast(device_type='cuda', dtype=torch.bfloat16):
+            hidden = model.language_model.model(attention_mask=attn_mask, inputs_embeds=inputs_embeds, use_cache=False).last_hidden_state
+
+        # Gather hidden states at verification positions
+        sample_idx, pos_idx, ctc_flat = [], [], []
+        sample_ranges, sample_valid = [], []
+        offset = 0
+
+        for i in range(batch_sz):
+            ctc_tokens = ctc_token_ids[i]
+            if not ctc_tokens or prompt_lens[i] - 1 + len(ctc_tokens) > hidden.shape[1]:
+                sample_ranges.append((offset, offset))
+                sample_valid.append(False)
+                continue
+            verify_start = prompt_lens[i] - 1
+            for k in range(len(ctc_tokens)):
+                sample_idx.append(i)
+                pos_idx.append(verify_start + k)
+                ctc_flat.append(ctc_tokens[k])
+            sample_ranges.append((offset, offset + len(ctc_tokens)))
+            sample_valid.append(True)
+            offset += len(ctc_tokens)
+
+        if pos_idx:
+            gathered = hidden[torch.tensor(sample_idx, device=device), torch.tensor(pos_idx, device=device), :]
+            with torch.amp.autocast(device_type='cuda', dtype=torch.bfloat16):
+                logits = model.language_model.lm_head(gathered) / logits_scaling
+            probs = F.softmax(logits.float(), dim=-1)
+            ctc_probs = probs[torch.arange(len(ctc_flat), device=device), torch.tensor(ctc_flat, device=device)]
+
+        results = []
+        for i in range(batch_sz):
+            s, e = sample_ranges[i]
+            if not sample_valid[i]:
+                results.append((False, ctc_texts[i]))
+                continue
+            token_probs = ctc_probs[s:e]
+            accepted = (token_probs >= confidence_threshold).all().item()
+            results.append((accepted, ctc_texts[i]))
+
+        return results, audio_embeds, proj_lengths
+
+    @torch.no_grad()
+    def fallback(audio_embeds, indices, proj_lengths):
+        """AR fallback for failed samples."""
+        if not indices:
+            return []
+
+        batch_sz = len(indices)
+        hidden_dim = audio_embeds.shape[-1]
+        all_embeds, all_lengths = [], []
+
+        for i in indices:
+            sample_embeds = audio_embeds[i, :proj_lengths[i], :].unsqueeze(0)
+            combined = torch.cat([cached_prefix_embeds, sample_embeds, cached_suffix_embeds], dim=1)
+            all_embeds.append(combined.squeeze(0))
+            all_lengths.append(combined.shape[1])
+
+        max_len = max(all_lengths)
+        padded = torch.zeros(batch_sz, max_len, hidden_dim, device=device, dtype=audio_embeds.dtype)
+        attn_mask = torch.zeros(batch_sz, max_len, dtype=torch.long, device=device)
+        for i, (emb, length) in enumerate(zip(all_embeds, all_lengths)):
+            padded[i, max_len - length:] = emb
+            attn_mask[i, max_len - length:] = 1
+
+        outputs = model.language_model.generate(
+            inputs_embeds=padded, attention_mask=attn_mask,
+            bos_token_id=tokenizer.bos_token_id, pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id, max_new_tokens=args.max_new_tokens,
+            num_beams=args.num_beams, early_stopping=args.num_beams > 1,
+            do_sample=False, use_cache=True
+        )
+
+        return [tokenizer.decode(outputs[i], skip_special_tokens=True) for i in range(batch_sz)]
+
+    def benchmark(batch):
+        audios = [audio["array"] for audio in batch["audio"]]
+        batch_sz = len(audios)
+
+        start_time = time.time()
+
+        # Step 1: CTC decode
+        ctc_texts, ctc_entropies, embeddings, embed_lengths = ctc_decode(audios)
+
+        # Step 2: Gate by CTC entropy
+        predictions = [None] * batch_sz
+        verify_idx = []
+
+        for i, (text, ent) in enumerate(zip(ctc_texts, ctc_entropies)):
+            if ent <= ctc_threshold and text.strip():
+                predictions[i] = text.strip()
+            else:
+                verify_idx.append(i)
+
+        # Step 3: Verify remaining
+        if verify_idx:
+            verify_emb = embeddings[verify_idx]
+            verify_lens = [embed_lengths[i] for i in verify_idx]
+            verify_texts = [ctc_texts[i] for i in verify_idx]
+
+            results, audio_embeds, proj_lengths = verify(verify_texts, verify_emb, verify_lens)
+
+            fail_idx = []
+            for j, (accepted, text) in enumerate(results):
+                i = verify_idx[j]
+                if accepted:
+                    predictions[i] = text.strip()
+                else:
+                    fail_idx.append(j)
+
+            # Step 4: Fallback
+            if fail_idx:
+                fallback_texts = fallback(audio_embeds, fail_idx, proj_lengths)
+                for k, j in enumerate(fail_idx):
+                    predictions[verify_idx[j]] = fallback_texts[k]
+
+        runtime = time.time() - start_time
+
+        batch["transcription_time_s"] = [runtime / batch_sz] * batch_sz
+        batch["predictions"] = [data_utils.normalizer(p) for p in predictions]
+        batch["references"] = batch["norm_text"]
+        return batch
+
+    # Load and process dataset
+    dataset = data_utils.load_data(args)
+    if args.max_eval_samples is not None and args.max_eval_samples > 0:
+        print(f"Subsampling to {args.max_eval_samples} samples")
+        dataset = dataset.select(range(min(args.max_eval_samples, len(dataset))))
+    dataset = data_utils.prepare_data(dataset)
+
+    dataset = dataset.map(benchmark, batch_size=args.batch_size, batched=True, remove_columns=["audio"], desc="Processing")
+
+    all_results = {"audio_length_s": [], "transcription_time_s": [], "predictions": [], "references": []}
+    for result in tqdm(dataset, desc="Samples"):
+        for key in all_results:
+            all_results[key].append(result[key])
+
+    # Write results
+    manifest_path = data_utils.write_manifest(
+        all_results["references"], all_results["predictions"], args.model_id,
+        args.dataset_path, args.dataset, args.split,
+        audio_length=all_results["audio_length_s"], transcription_time=all_results["transcription_time_s"]
+    )
+    print("Results saved at:", os.path.abspath(manifest_path))
+
+    wer = round(100 * wer_metric.compute(references=all_results["references"], predictions=all_results["predictions"]), 2)
+    rtfx = round(sum(all_results["audio_length_s"]) / sum(all_results["transcription_time_s"]), 2)
+    print(f"WER: {wer}%, RTFx: {rtfx}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_id", type=str, required=True)
+    parser.add_argument("--dataset_path", type=str, default="esb/datasets")
+    parser.add_argument("--dataset", type=str, required=True)
+    parser.add_argument("--split", type=str, default="test")
+    parser.add_argument("--device", type=int, default=-1)
+    parser.add_argument("--batch_size", type=int, default=16)
+    parser.add_argument("--max_eval_samples", type=int, default=None)
+    parser.add_argument("--max_new_tokens", type=int, default=200)
+    parser.add_argument("--num_beams", type=int, default=1)
+    parser.add_argument("--confidence_threshold", type=float, default=0.01)
+    parser.add_argument("--ctc_threshold", type=float, default=0.5)
+    parser.add_argument("--no-streaming", dest="streaming", action="store_false")
+    args = parser.parse_args()
+    args.streaming = False
+    main(args)
diff --git a/granite/run_speculative.sh b/granite/run_speculative.sh
new file mode 100644
index 0000000..009770a
--- /dev/null
+++ b/granite/run_speculative.sh
@@ -0,0 +1,122 @@
+#!/bin/bash
+
+export PYTHONPATH="..":$PYTHONPATH
+
+MODEL_ID="ibm-granite/granite-4.0-1b-speech"
+BATCH_SIZE=256
+NUM_BEAMS=2
+MAX_NEW_TOKENS=200
+
+# Speculative decoding thresholds
+CONFIDENCE_THRESHOLD=0.2
+CTC_THRESHOLD=0.7
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="voxpopuli" \
+    --split="test" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD}
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="ami" \
+    --split="test" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD}
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="earnings22" \
+    --split="test" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD}
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="gigaspeech" \
+    --split="test" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD} 
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="librispeech" \
+    --split="test.clean" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD} 
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="librispeech" \
+    --split="test.other" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD} 
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="spgispeech" \
+    --split="test" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD} 
+
+python run_eval_speculative.py \
+    --model_id=${MODEL_ID} \
+    --dataset_path="hf-audio/esb-datasets-test-only-sorted" \
+    --dataset="tedlium" \
+    --split="test" \
+    --device=0 \
+    --batch_size=${BATCH_SIZE} \
+    --num_beams=${NUM_BEAMS} \
+    --max_eval_samples=-1 \
+    --max_new_tokens=${MAX_NEW_TOKENS} \
+    --confidence_threshold=${CONFIDENCE_THRESHOLD} \
+    --ctc_threshold=${CTC_THRESHOLD}
+
+# Evaluate results
+RUNDIR=`pwd` && \
+cd ../normalizer && \
+python -c "import eval_utils; eval_utils.score_results('${RUNDIR}/results', '${MODEL_ID}')" && \
+cd $RUNDIR