fix(cohere): correct host-side mel features + CJK detokenization (resolves 71% FLEURS failure)

[Alex-Wengg]

⚠️ Update (post-Devin fixes): real root cause of the 71% FLEURS failure

The "Known Limitations" section below (preserved for history) attributes the 71% FLEURS failure rate to a training bias. That attribution was wrong. The encoder and decoder weights are fine. The host-side preprocessing pipeline was producing features from a different distribution than the one the encoder was trained on, and the CJK detokenizer was not handling SentencePiece byte fallback.

After four host-only fixes (no retraining, same model weights), the FLEURS repetition-loop failures disappear and multilingual WER drops dramatically.

Benchmark (FLEURS, 3 samples × 4 languages, same CoreML model files)

Language	Metric	OLD pipeline	NEW pipeline	Δ
en_us	WER	55.3%	10.6%	−44.6pp
es_419	WER	11.3%	4.9%	−6.4pp
fr_fr	WER	92.1%	16.8%	−75.2pp
cmn_hans_cn	CER	261.7%	14.1%	−247.6pp

Sample outputs (same encoder+decoder weights):

• French, OLD: اذا شرطكم الجلوس وغيرهم من الشمس ومن الشمس... (Arabic hallucination, 100% WER)
• French, NEW: Il a ajouté qu'on ne devrait cependant pas leur demander d'assumer des obligations... (23% WER, standard ASR errors)
• Chinese sample 0, OLD: To tylko szybko odkryć. To szybko kędzamy cieszą... (Polish hallucination, 261% CER)
• Chinese sample 0, NEW: 这并不是告别:这是一个篇章的结束,也是新篆竿的开始。 (13% CER; only 篆竿 wrong)

Why the old output was Arabic / Polish

The shipped cohere_mel_spectrogram.py did not match processing_cohere_asr.py::FilterbankFeatures on any parameter that matters: wrong n_fft (1024 vs. 512), wrong window (librosa default vs. Hann(400) padded to 512), wrong mel normalization (librosa default vs. Slaney), wrong log (log10 + (mel+80)/80 vs. natural log with 2^-24 guard), and no per-feature CMVN at all. Without CMVN every utterance's features drift by tens of dB per bin, so the encoder receives input that lies nowhere in its training manifold. The decoder then emits whatever language cluster happened to be nearest — for this checkpoint, that's Arabic/Polish. This is classic out-of-distribution failure, not a training artifact.

Fixes (this commit set)

1. tools/cohere_features_v2.py — faithful numpy port of FilterbankFeatures: n_fft=512, Hann(400) zero-padded to 512, preemph=0.97, Slaney mel, natural log + 2^-24 guard, per-feature CMVN (ddof=1, ε=1e-5), mag_power=2.0. Verified vs. AutoFeatureExtractor.from_pretrained(..., trust_remote_code=True) on 5 real samples × 4 languages: residual is within HF's own dither variance (max 0.70, mean 1.8e-3 with dither disabled).
2. Cross-attention mask respects feature_length — the encoder always emits 438 frames but only ceil(feature_length * 438/3500) of them correspond to real audio. Padded encoder frames are now masked with −1e4 in the decoder's cross-attention instead of being attended to.
3. Repetition penalty + no-repeat-ngram in greedy decode — defaults repetition_penalty=1.1, no_repeat_ngram=3. Breaks any residual loops (mostly unneeded once features are correct, but cheap insurance).
4. SentencePiece byte-fallback detokenization — the tokenizer has no single piece for most CJK characters; 篇 is emitted as <0xE7><0xAF><0x87> (its UTF-8 encoding). tokens_to_text now buffers consecutive <0xHH> pieces and flushes them through bytes(...).decode("utf-8", errors="replace").

Files added/changed

• tools/cohere_features_v2.py (new) — canonical numpy port
• f16/cohere_mel_spectrogram.py (replaced) — v2 content, shipped standalone
• q8/cohere_mel_spectrogram.py (replaced) — v2 content, shipped standalone
• f16/example_inference.py (updated) — correct extractor, masked cross-attn, repetition penalty, byte-fallback CJK detok
• q8/example_inference.py (updated) — mirrors f16
• tests/test-feature-parity.py (new) — numpy vs HF AutoFeatureExtractor parity proof
• tests/diagnose-feature-diff.py (new) — isolates dither noise as the residual error source
• tests/bench-fix-vs-broken.py (new) — end-to-end A/B benchmark with CER for CJK

What this means for the original "Critical Fixes" section

The 9 Devin-review items below are mostly cosmetic on a pipeline that was already producing nonsense features. They didn't regress anything, but they also didn't fix the headline problem. The actual cause was never mentioned in any review.

What this does not cover

No changes to exports/export-encoder.py, exports/export-decoder-stateful.py, or the HuggingFace-uploaded .mlpackage files. The encoder and decoder ship as-is; all fixes are host-side Python.

Q8 verification against HF-shipped .mlpackage files

Downloaded q8/ from FluidInference/cohere-transcribe-03-2026-coreml and ran the same fixed pipeline against the uploaded stateful decoder (tests/bench-q8-fleurs.py). Purpose: confirm on the actual files that users install that the host-side fix eliminates the language-hallucination failure.

Result: it does. Q8 outputs are the correct language with recognizable transcripts — no Arabic-for-French or Polish-for-Chinese on any of the 12 samples.

FLEURS, 3 samples × 4 languages, fixed pipeline vs uploaded .mlpackage:

Language	Metric	f16 (local)	q8 (HF download)
en_us	WER	10.6%	73.4%
es_419	WER	4.9%	23.3%
fr_fr	WER	16.8%	45.2%
cmn_hans_cn	CER	14.1%	48.3%

The q8 decoder has a separate, orthogonal failure mode: over-generation. It produces a correct transcript, then keeps going and hallucinates additional content past the true end of the utterance. Examples (all from q8, all with no_repeat_ngram=3 active so these are not simple repetition loops):

• EN sample 0: correct → then (Thanks for the lack of a better word) appended
• FR sample 0: L'accident a eu lieu en terrain montagneux, et il semblerait que cela ait été causé par un incendie malveillant. (correct) → then appends (This is the case of a man with a man-made lampadaire, a été causée par un accident malveilant.)
• CN sample 2: correct Chinese transcript → then appends Korean-looking garbage

The decoder stops eventually, but only via max-token cap, not via emitting EOS. This is consistent with INT8 quantization degrading the EOS logit margin. It is out of scope for this PR (same problem existed with the broken pipeline, it just wasn't visible under the sea of OOD hallucinations), and aligns with the PR's own QUANTIZATION_RESULTS.md recommendation: use FP16 decoder + (optionally) q8 encoder, not a fully-q8 pipeline.

Q8 root-cause investigation: EOS is not suppressed, it's losing by 2 logits

I instrumented the q8 stateful decoder (tests/probe-q8-eos.py) to dump the logit of every token at every step, together with the rank of EOS. The pattern is clean and not what the "out of scope" comment above assumed.

At the true end-of-sentence boundary, EOS is rank 1 or 2 (i.e. second- or third-most-likely token). The gap between EOS and the winning token is ~2-3 logit units, not 20+. Example from the FR sample at step 47 (the token that should have been EOS, right after the closing period of ...incendie malveillant.):

step   tok piece        top1   top1_lg   eos_lg  eos_rnk  eos_gap
  47 13764 _            13764   21.250   18.688       1     2.562

_ (a leading-space token) beat EOS by 2.56 logits. That margin is inside the noise band of weight-only INT8 quantization on a per-channel linear layer. Once the decoder steps past the period, it locks into a benign-looking text continuation and the same 2-logit "just barely not EOS" pattern persists for the rest of the trajectory:

step 85 _with  top1_lg=15.828  eos_lg=13.414  eos_rnk=1  eos_gap=2.414
step 97 _with  top1_lg=15.641  eos_lg=13.891  eos_rnk=1  eos_gap=1.750
step 103 _with top1_lg=15.969  eos_lg=14.406  eos_rnk=1  eos_gap=1.562

In other words, EOS is always the runner-up. The decoder wants to stop, but is consistently being beaten by ~2 logits. This is textbook weight-only INT8 behavior for a final classification layer: quantization adds small, systematic error to each vocab logit, and vocabulary entries that are close to the winner get flipped.

One-line mitigation: bias the EOS logit by +4

Because the margin is small and systematic, a flat additive bias on the EOS logit inside the greedy loop restores quality almost completely. Sweep over the same 12-sample FLEURS slice (tests/bench-q8-eosboost.py):

Language	+0.0	+2.0	+4.0	f16 (reference)
en_us WER	73.4%	22.2%	13.4%	10.6%
es_419 WER	23.3%	3.6%	3.6%	4.9%
fr_fr WER	45.2%	31.8%	13.5%	16.8%
cmn_hans_cn CER	48.3%	14.1%	14.1%	14.1%

With eos_bias=+4.0 the q8 decoder matches or beats f16 on every language in the slice. No retraining, no re-export. One line of Python: logits[3] += 4.0 before argmax.

Other observations:

• No evidence of premature EOS at +4.0. Spanish average token count stays at 58.7 (vs 58.7 at +2.0). Chinese: 36.7 for both +2 and +4.
• The "+2.0 is enough" languages (ES, ZH) are ones where the model already had a larger EOS margin in the un-quantized model; INT8 noise only marginally hid it.
• The "+4.0 needed" languages (EN, FR) are ones where even the FP16 decoder probably had small EOS margins at punctuation boundaries and INT8 noise tipped them over.

Proper fix (out of scope for this PR): re-quantize the decoder with output-layer-aware calibration, or keep the final lm_head Linear at FP16 while INT8-ing the body. Either would restore the EOS logit margin without a host-side hack. For now, users running the q8 pipeline should apply +3 to +4 EOS bias — see tests/bench-q8-eosboost.py.

Q8 re-quantization experiments — quality loss is not just in lm_head

The EOS-bias diagnosis suggests the lm_head logit layer is the culprit. To test that claim I downloaded the FP16 decoder (cohere_decoder_stateful.mlpackage, 290 MB) and re-ran coremltools.optimize.coreml.linear_quantize_weights with three targeted configs (tests/requantize-decoder.py), then benchmarked each new variant on the same 12-sample FLEURS slice with no EOS bias (tests/bench-q8-variants.py).

Important finding about the decoder architecture: the embedding is tied. coremltools.optimize.coreml.get_weights_metadata (tests/inspect-f16-decoder.py) shows one const, embedding_token_embedding_weight_to_fp16 (shape (16384, 1024), 16.7M parameters), that feeds two ops: op_341_cast_fp16_cast_uint16 (gather for input embedding) and linear_80_cast_fp16 (lm_head). Any op_name_configs override must be applied to both consumers or linear_quantize_weights raises ValueError: compression config conflict detected between ops. This constraint is why "skip only the lm_head" is not physically expressible — if you skip quantization on the linear, you have to skip it on the gather too (both consumers of the shared const must agree).

Variants produced:

variant	config	tied embedding	everything else	size
baseline_q8 (shipped)	per-channel INT8, everything	INT8 per-channel	INT8 per-channel	135 MB
skip_lmhead	per-channel INT8, skip tied const	FP16	INT8 per-channel	158 MB
per_tensor_lmhead	per-channel INT8 body, per-tensor on tied const	INT8 per-tensor	INT8 per-channel	142 MB
threshold_big	per-channel INT8, weight_threshold=2_000_000 + skip tied	FP16	INT8 per-channel for >2M, FP16 for ≤2M (skips QKV projections, 1M each)	221 MB

Results (same 12 FLEURS samples, no EOS bias):

lang	baseline_q8	skip_lmhead	per_tensor_lmhead	threshold_big
en_us WER	73.4%	80.5%	43.6%	51.2%
es_419 WER	23.3%	23.3%	18.8%	23.3%
fr_fr WER	45.2%	45.2%	26.9%	45.2%
cmn_hans_cn CER	48.3%	48.3%	46.8%	48.3%

Interpretation — the lm_head story was incomplete:

1. skip_lmhead (lm_head at FP16) does not help and actually hurts English. If the EOS logit margin were dominated by lm_head quantization noise, this should have been the fix. It isn't. The tied embedding is already a pretty clean INT8 target; per-channel scaling of a (16384, 1024) matrix has per-row scales that track each vocab entry reasonably.
2. per_tensor_lmhead (single shared INT8 scale for the tied embedding) is the clear winner — English 73→44%, French 45→27%, Spanish 23→19%, Chinese small improvement. Per-tensor quantization increased per-row error (one scale for all 16384 rows) but reduced relative error across rows, which is what EOS-vs-top1 comparisons actually need.
3. threshold_big helped only English (73→51%). The ops it additionally skipped (1M-numel QKV projections) matter for English more than for other languages, but the gain is small.
4. None of these come close to the EOS-bias workaround (EN 13.4%, FR 13.5%). The q8 quality loss is distributed across many layers, not localized to lm_head. The +4 EOS bias isn't just compensating for lm_head noise — it's compensating for accumulated per-channel quantization error in the FFN and attention stacks that happens to manifest on the EOS logit margin.

Recommended production path: for now, keep shipping the current q8 weights and apply the +3 to +4 EOS bias at runtime. A proper quantization-side fix would need either (a) calibration-aware quantization with a dataset that includes end-of-utterance frames so the optimizer can protect the EOS logit gap, or (b) mixed-precision with the FFN layers in INT8 but the attention output projections (which shape the logit distribution) kept at FP16 — neither is expressible through coremltools.optimize.coreml's op-level API without per-op calibration, so that's its own project.

Artifacts:

• tests/inspect-f16-decoder.py — find tied-embedding op names
• tests/requantize-decoder.py — produce three variants from FP16 decoder
• tests/bench-q8-variants.py — 12-sample FLEURS comparison of all four

---

Summary

Complete CoreML conversion pipeline for Cohere Transcribe, a 14-language ASR model with encoder-decoder architecture. Includes FP16 and INT8 quantized models optimized for Apple Neural Engine.

🔧 Now includes comprehensive fixes for 9 critical issues identified in Devin AI review.

---

Critical Fixes (Latest Commits)

✅ Correctness Issues Fixed

1. Language Token IDs - All non-English languages now use correct token IDs (was hardcoded to English)
2. Encoder Parameter Typo - Feature length masking now applied (length vs lengths)
3. Decoder Log-Softmax - Returns log-probabilities for beam search compatibility
4. EOS Token Fallback - Uses correct token ID 3 instead of 2
5. Mel Padding - Fixed 35-second window (3500 frames, was 3001)
6. Operator Precedence - Cache assignments validate tensor dimensions correctly
7. Autoregressive Validation - Multi-step test now feeds predicted tokens

✅ Process Issues Fixed

8. uv.lock Committed - Reproducible dependency versions
9. Project Name - Fixed pyproject.toml (was "parakeet-coreml")

See commit history for detailed changes:

• 887b22b - Critical correctness issues
• 395e48a - Test file issues
• f81dfb7 - Decoder export issues
• 8c95861 - Reproducibility

---

What This PR Adds

CoreML Export Pipeline

• Encoder: Mel spectrogram → 438 encoder outputs (35-second window)
• Decoder: Stateful decoder with CoreML State API (macOS 15+)
• Quantization: INT8 W8A16 conversion (~2.0 GB vs ~4.2 GB FP16)

Export Scripts (exports/, tools/)

• export-encoder.py - Export encoder to CoreML (35-second window)
• export-decoder-stateful.py - Stateful decoder with CoreML State API + log-softmax
• quantize_to_int8.py - INT8 quantization pipeline
• export-encoder-ios18.py - iOS 18+ encoder for INT4 quantization experiments

Testing & Benchmarking

• tests/benchmark-models.py - Model quality validation
• tests/compare-models.py - PyTorch vs CoreML parity check
• tests/measure-memory.py - Memory profiling
• benchmark.py - LibriSpeech evaluation
• benchmark_all_languages.py - Multi-language testing
• benchmark_cjk_cer.py - CER metrics for Chinese/Japanese/Korean

Quantization Research (QUANTIZATION_RESULTS.md)

Comprehensive comparison of FP16, INT8, INT4, and hybrid configurations:

• Recommended: INT8 encoder + FP16 decoder (46% size reduction, same quality)
• Rejected: INT4 (293% avg WER with hallucinations)
• Rejected: INT8 decoder (71% repetition loops)

---

Model Quality

INT8 Results (LibriSpeech test-clean, 100 samples)

• Average WER: 16.44%
• Perfect matches: 50%
• Good (<30% WER): 80%
• RTFx: ~0.25x (real-time capable)

14 Languages Supported

English, Spanish, French, German, Italian, Portuguese, Polish, Dutch, Swedish, Turkish, Russian, Chinese, Japanese, Korean

---

Architecture Details

35-Second Window Design

• Input: 3500 mel frames (35 seconds @ 10ms stride)
• Encoder output: 438 hidden states (1, 438, 1024)
• Decoder: Stateful with CoreML State API for KV cache
• Max tokens: 108 per window

Language Token Conditioning (FIXED)

Language selection via 10-token primer sequences with correct token IDs:

LANGUAGE_PROMPTS = {
    "en": [13764, 7, 4, 16, 62, 62, 5, 9, 11, 13],    # English (token 62)
    "es": [13764, 7, 4, 16, 169, 169, 5, 9, 11, 13],  # Spanish (token 169)
    "fr": [13764, 7, 4, 16, 69, 69, 5, 9, 11, 13],    # French (token 69)
    # ... etc for 14 languages
}

Stateful Decoder Implementation

Uses CoreML State API with log-softmax output for GPU-resident KV cache:

• Requires macOS 15+ (.mlpackage only, no .mlmodelc)
• Zero-copy state management
• Fixed 108-token cache window
• Returns log-probabilities (enables beam search)

---

Known Limitations

FLEURS Dataset Incompatibility (SUPERSEDED — see Update section at top)

Original claim retained for history. The "training bias" diagnosis was wrong; see the Update section for the actual root cause (broken host-side feature extraction) and the post-fix benchmark numbers.

Testing revealed decoder repetitive loops in 71% of FLEURS samples:

• LibriSpeech: 80% success rate (clean studio audio)
• FLEURS: 20% success rate (diverse audio triggers loops)

Common failure patterns:

• "the the the..." (660% WER)
• "extremism, extremism, extremism..." (530% WER)

Root cause: Model training bias toward louder, lower-pitched voices. Not a CoreML conversion issue (PyTorch has identical behavior).

---

Files Changed

Conversion Pipeline

• exports/export-encoder.py - Encoder export with correct length parameter
• exports/export-decoder-stateful.py - Stateful decoder with log-softmax + autoregressive validation
• export-encoder-ios18.py - iOS 18 encoder for INT4 experiments
• tools/quantize_to_int8.py - INT8 quantization

Inference Examples

• f16/example_inference.py - FP16 inference with correct language tokens
• q8/example_inference.py - INT8 inference with correct language tokens
• f16/cohere_mel_spectrogram.py - Mel preprocessing
• q8/cohere_mel_spectrogram.py - Mel preprocessing

Testing (All Fixed)

• tests/benchmark-models.py - Correct EOS token (3), 3500-frame padding
• tests/compare-models.py - Fixed operator precedence, 3500-frame padding
• tests/measure-memory.py - 3500-frame padding

Documentation

• QUANTIZATION_RESULTS.md - Comprehensive quantization analysis
• RESEARCH_INSIGHTS.md - Recent ASR research papers
• STATELESS_VS_STATEFUL.md - Decoder architecture comparison
• MLMODELC_LIMITATION.md - State API .mlpackage requirement

Configuration

• pyproject.toml - Fixed project name ("cohere-transcribe-coreml")
• .gitignore - Removed uv.lock exclusion
• uv.lock - Committed for reproducibility (4725 lines)

---

HuggingFace Upload

Models uploaded to: https://huggingface.co/FluidInference/cohere-transcribe-03-2026-coreml

Directory structure:

f16/                          # FP16 models (~4.2 GB)
├── cohere_encoder.mlpackage
├── cohere_decoder_stateful.mlpackage
├── vocab.json
└── example_inference.py      # Fixed language tokens

q8/                           # INT8 models (~2.0 GB)
├── cohere_encoder.mlpackage
├── cohere_decoder_stateful.mlpackage
├── vocab.json
└── example_inference.py      # Fixed language tokens

---

Integration

Swift integration in FluidAudio: FluidInference/FluidAudio#487

• Hybrid quantization (INT8 encoder + FP16 decoder)
• Automatic model download from HuggingFace
• 14-language support

---

Test Plan

• Encoder export to CoreML with correct parameter names
• Stateful decoder export with log-softmax output
• INT8 quantization (W8A16)
• INT4 quantization experiments (rejected due to quality)
• LibriSpeech benchmark: 16.44% WER (INT8)
• Multi-language verification with correct token IDs
• PyTorch vs CoreML parity validation
• HuggingFace upload (FP16 and INT8)
• Swift integration in FluidAudio
• Devin AI review issues addressed (9/9 critical)
• uv.lock committed for reproducibility
• Full 14-language FLEURS benchmark (blocked by model limitations)

---

Review Notes

All 9 critical issues identified in Devin AI reviews have been addressed:

1. ✅ Language token IDs fixed (all 14 languages)
2. ✅ Encoder parameter name corrected
3. ✅ Decoder log-softmax added
4. ✅ EOS token fallback corrected
5. ✅ Mel padding fixed to 3500 frames
6. ✅ Operator precedence bug fixed
7. ✅ Autoregressive validation fixed
8. ✅ uv.lock committed
9. ✅ Project name corrected

Two remaining issues are in PyTorch training code (not CoreML inference):

• Buffer registration in preprocessing (affects multi-GPU training)
• Double log-softmax in fine-tuning loss (affects gradient computation)

These do not impact CoreML conversion or inference quality.

---

🤖 Generated with Claude Code

[devin-ai-integration]

Devin Review found 4 new potential issues.

🐛 1 issue in files not directly in the diff

🐛 Cache truncation drops newly appended token, making KV cache permanently empty (models/stt/cohere-transcribe-03-2026/coreml/hf-upload/export-decoder-cached.py:110-112)

The HuggingFace-published cached decoder truncates the updated cache to the first max_seq_len (108) positions after DynamicCache appends 1 new entry (making 109 total). Since DynamicCache appends new KV entries at the END, the new token's KV is at position 108 (0-indexed) and layer_k[:, :self.max_seq_len, :] (i.e., layer_k[:, :108, :]) drops it. This means the output cache after every step is just the input cache with the newest token's information lost — the cache never accumulates any real data. This is distinct from the archived sliding-window bug (layer_k[:, -self.max_seq_len:, :]) but has a similarly devastating effect: the decoder produces garbage because no token history is retained. The same truncation bug exists in hf-upload/export-decoder-with-cross-kv.py:129-131. The hf-upload/README.md presents this decoder as the primary working model without mentioning it's broken.

View 8 additional findings in Devin Review.

[devin-ai-integration]

🟡 Operator precedence bug causes incorrect cache output assignment

Due to Python operator precedence (and binds tighter than or), the conditions on lines 164 and 166 are parsed as (len(value.shape) == 4 and 'cache_k' in key.lower()) or (key == 'new_cache_k'). This means if the output key is exactly 'new_cache_k', the value is assigned to our_cache_k regardless of whether it has 4 dimensions. The same issue exists on line 166 for cache_v. The intended logic was likely len(value.shape) == 4 and ('cache_k' in key.lower() or key == 'new_cache_k'), requiring parentheses around the or clause.

Suggested change

	elif len(value.shape) == 4 and 'cache_k' in key.lower() or key == 'new_cache_k':
	our_cache_k = value
	elif len(value.shape) == 4 and 'cache_v' in key.lower() or key == 'new_cache_v':
	elif len(value.shape) == 4 and ('cache_k' in key.lower() or key == 'new_cache_k'):
	our_cache_k = value
	elif len(value.shape) == 4 and ('cache_v' in key.lower() or key == 'new_cache_v'):

---

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[devin-ai-integration]

🟡 pyproject.toml has wrong project name from copy-paste

The pyproject.toml has name = "parakeet-coreml" which is copied from a different model's project configuration. This should be something like "cohere-transcribe-coreml" to match the actual model being converted.

Suggested change

	name = "parakeet-coreml"
	name = "cohere-transcribe-coreml"

---

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[devin-ai-integration]

Devin Review found 2 new potential issues.

View 11 additional findings in Devin Review.

[devin-ai-integration]

🟡 Wrong EOS token fallback: uses pad_token_id (2) instead of eos_token_id (3)

When the tokenizer fails to load, the EOS token falls back to 2 (the pad token) instead of 3 (the actual EOS token). Every other file in this PR consistently uses EOS_TOKEN_ID = 3 (test-stateless-coreml.py:17, test-stateful-decoder.py:27, test-librispeech.py:19, hf-upload/README.md:75), and the generation config at docs/OFFICIAL_USAGE_ANALYSIS.md:103 confirms "eos_token_id": 3. With the wrong fallback, the decoder loop would fail to stop at the correct token when the processor is unavailable, potentially generating garbage until max_new_tokens is hit, or stopping prematurely if token 2 appears in the output.

Suggested change

	self.eos_token_id = processor.eos_token_id if processor else 2
	self.eos_token_id = processor.eos_token_id if processor else 3

---

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[devin-ai-integration]

Devin Review found 2 new potential issues.

View 15 additional findings in Devin Review.

[devin-ai-integration]

🔴 benchmark-models.py pads mel to 3001 frames but encoder expects 3500 frames

The encoder was re-exported with max_frames = 3500 (export-encoder.py:79) to support the official 35-second window, but benchmark-models.py still hardcodes padding to 3001 frames at line 63. This causes two issues: (1) for audio longer than ~30s, 3001 - mel.shape[2] becomes negative, crashing with a numpy padding error; (2) for shorter audio, the encoder receives 3001-padded input instead of the expected 3500, producing mismatched hidden state dimensions. The same stale value also appears in compare-models.py:33, measure-memory.py:65, and test_stateful_long_audio.py:75.

---

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[devin-ai-integration]

🔴 Stateful decoder export omits log_softmax, producing raw logits instead of log probabilities

The stateful decoder extracts only the raw Linear layer (model.log_softmax.mlp.layer0) at export-decoder-stateful.py:243, whereas the original model's TokenClassifierHead applies torch.log_softmax when config.head.log_softmax is true (which it is per config.json:57). This means StatefulCohereDecoder.forward() at line 148 returns raw logits instead of log probabilities. In contrast, the stateless decoder correctly uses the full TokenClassifierHead (full_model.log_softmax at export-decoder-stateless.py:29). While greedy argmax decoding produces identical token selections (since log_softmax is monotonic), any beam search, sampling, or probability-threshold–based processing will produce incorrect results because the output scale is wrong.

Prompt for agents

The stateful decoder extracts only model.log_softmax.mlp.layer0 (a bare nn.Linear) as lm_head, but the original model's TokenClassifierHead applies torch.log_softmax after the linear layer when config.head.log_softmax is true (which it is in config.json). The stateless decoder correctly uses full_model.log_softmax.

To fix this, change line 243 in export-decoder-stateful.py from:
  lm_head = model.log_softmax.mlp.layer0
to:
  lm_head = model.log_softmax

Then in the StatefulCohereDecoder class, self.lm_head will be the full TokenClassifierHead and forward() will correctly apply log_softmax. Verify that the lm_head variable name still makes sense and update comments/docstrings as needed. Also check that the traced model validation and CoreML conversion still work correctly with the full TokenClassifierHead module.

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[devin-ai-integration]

Devin Review found 1 new potential issue.

View 21 additional findings in Devin Review.

[devin-ai-integration]

🔴 Wrong parameter name lengths silently ignored by encoder's **kwargs, causing feature_length input to be unused

In the CoreML encoder export wrapper, the encoder is called with lengths=feature_length (line 37), but ConformerEncoder.forward() accepts the parameter as length (not lengths). Since the encoder's forward signature includes **kwargs (modeling_cohere_asr.py:415), the misspelled kwarg lengths is silently consumed by **kwargs and discarded. The encoder then falls back to the length=None default path (modeling_cohere_asr.py:419-425), which creates a length tensor from input_features.shape[-1] — treating all padding as real audio. This means the feature_length input to the exported CoreML encoder model is accepted but never actually used; the encoder always processes the entire padded input without proper attention masking for shorter audio.

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[BrandonWeng]

no lfs pls. do not commit here