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content: https://huggingface.co/blog/CohereLabs/cohere-transcribe-03-2026-release can we perform research that this model can be finetuned ? like can this be finetuned and can this be even attempted. cause this is a conformer based model, can it be finetuned. can you research this. and let me know how can this be done. if any such recipes, or huggingface model card, readme cards, or anythingg related to this model finetuning.

permissionMode: bypassPermissions

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type: text

text: Let me research this thoroughly.


Agent

description: Research Cohere Transcribe finetuning

prompt:
Research the Cohere Transcribe model released in March 2026. I need to understand:

1. First, fetch this blog post: https://huggingface.co/blog/CohereLabs/cohere-transcribe-03-2026-release — extract all details about the model architecture, weights availability, license, and any mention of fine-tuning.

2. Search the web for: "Cohere Transcribe fine-tune" OR "Cohere Transcribe finetune" — any guides, recipes, or discussions.

3. Search the web for: "CohereLabs/cohere-transcribe" huggingface model card — check if the model weights are publicly available on HuggingFace.

4. Search the web for: "Cohere Transcribe conformer fine-tuning" — since it's conformer-based, check if there are any conformer fine-tuning recipes that could apply.

5. Search for the actual HuggingFace model page: https://huggingface.co/CohereLabs — check what models they have published and whether weights are downloadable.

6. Search for any GitHub repos, issues, or discussions about fine-tuning this model.

7. Check if Cohere provides any fine-tuning API or documentation for their speech models.

Report back ALL findings in detail — especially:
- Are the model weights publicly available for download?
- What is the license?
- Is there any official or community fine-tuning guide?
- What framework does it use (NeMo, HuggingFace Transformers, custom)?
- Any blockers to fine-tuning?

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text:
I now have comprehensive information. Here is the full research report.

---

## Cohere Transcribe (cohere-transcribe-03-2026) -- Full Research Report

### 1. Model Architecture

- **Type:** Encoder-decoder with asymmetric design
- **Encoder:** Fast-Conformer (based on [Fast Conformer with Linearly Scalable Attention](https://arxiv.org/abs/2305.05084)) -- contains >90% of all parameters
- **Decoder:** Lightweight X-attention Transformer -- contains <10% of parameters
- **Total parameters:** 2 billion (2B)
- **Training objective:** Supervised cross-entropy on output tokens, trained from scratch
- **Training data:** 0.5M hours of curated audio-transcript pairs, augmented with synthetic data
- **Tokenizer:** 16k multilingual BPE with byte fallback
- **Input:** Audio waveform resampled to 16kHz mono, converted to log-Mel spectrogram
- **Languages:** 14 -- English, German, French, Italian, Spanish, Portuguese, Greek, Dutch, Polish, Arabic, Vietnamese, Chinese, Japanese, Korean

### 2. Weights Availability

**YES -- fully publicly available for download.**

- HuggingFace model page: `https://huggingface.co/CohereLabs/cohere-transcribe-03-2026`
- Weights are in a single `model.safetensors` file
- 50,497 downloads as of today
- 634 likes
- The model repo is marked `private: false`
- Custom code files included: `modeling_cohere_asr.py`, `configuration_cohere_asr.py`, `processing_cohere_asr.py`, `tokenization_cohere_asr.py`
- Requires `trust_remote_code=True` for the broader compatibility path, or `transformers>=5.4.0` for native support via `CohereAsrForConditionalGeneration`

### 3. License

**Apache 2.0** -- fully permissive, allows commercial use, modification, and redistribution.

### 4. Framework

- **Primary:** HuggingFace Transformers (`transformers>=5.4.0` for native support)
- **Custom model class:** `CohereAsrForConditionalGeneration` (registered as `cohere_asr` architecture type)
- **Production serving:** vLLM with optimized encoder-decoder support (see [vLLM PR #38120](https://github.com/vllm-project/vllm/pull/38120))
- **Additional runtimes:** mlx-audio (Apple Silicon), transformers.js + WebGPU (browser), `cohere_transcribe_rs` (Rust)
- **NOT NeMo-based** despite using Fast-Conformer architecture -- it uses custom HuggingFace Transformers code
- **Dependencies:** `transformers>=5.4.0`, `torch>=2.10.0`, `soundfile`, `librosa`, `sentencepiece`, `protobuf`

### 5. Fine-Tuning Status -- THE KEY QUESTION

**No official fine-tuning guide, script, or documentation exists as of 2026-03-31.**

Here is the evidence:

- The blog post makes zero mention of fine-tuning
- The model card provides only inference code, no training code
- HuggingFace discussion thread #9 ("Fine tuning code availability?") was opened 5 days ago with 9 upvotes and **no response from Cohere Labs**
- The `cohere-finetune` GitHub repo (`github.com/cohere-ai/cohere-finetune`) is for Cohere's text LLMs only, not speech models
- Cohere's fine-tuning API/dashboard covers text models (Command R, etc.) and is actually being retired -- no speech model fine-tuning is offered
- The community repo CohereX (`github.com/Diffio-AI/CohereX`) is a wrapper for inference with VAD + forced alignment -- no training scripts
- No third-party fine-tuning guides or blog posts were found anywhere

### 6. Potential Paths to Fine-Tuning (Analysis)

Since the model uses HuggingFace Transformers with a custom `CohereAsrForConditionalGeneration` class:

1. **Standard HF Trainer approach** should theoretically work -- the model class inherits from standard HF base classes, weights are in safetensors format, and the architecture is a standard encoder-decoder. You could write a training loop using `Trainer` or `Seq2SeqTrainer` with appropriate data collation.

2. **LoRA/PEFT** could likely be applied since it uses standard PyTorch modules. The model files (`modeling_cohere_asr.py`) define the architecture in pure PyTorch.

3. **Blockers/Risks:**
   - No reference training config or hyperparameters published
   - The custom processor/tokenizer code may have training-specific requirements not documented
   - At 2B parameters, full fine-tuning requires significant GPU memory (likely 40-80GB+)
   - The data pipeline (audio preprocessing, chunking for long-form) would need to be reverse-engineered from the processor code
   - No community has attempted/published results yet (model is only ~1 week old)

4. **Fast-Conformer heritage:** The architecture originates from NVIDIA's NeMo ecosystem, but Cohere reimplemented it in pure HF Transformers. NeMo's conformer fine-tuning recipes would NOT directly apply -- you would need to adapt them to the HF Transformers training paradigm.

### 7. Performance Benchmarks (for context)

- **#1 on Open ASR Leaderboard** with 5.42% average WER across 8 English benchmarks
- Beats Whisper Large v3 (7.44%), NVIDIA Canary Qwen 2.5B (5.63%), IBM Granite 4.0 1B (5.52%)
- LibriSpeech Clean: 1.25% WER; LibriSpeech Other: 2.37% WER
- 3x faster offline throughput than similarly-sized competitors
- Supports 14 languages with strong multilingual performance

### 8. Known Limitations

- No timestamps (word-level or segment-level) built in
- No speaker diarization
- No automatic language detection -- language must be specified
- Eager to transcribe non-speech sounds (hallucination risk without VAD)
- Code-switching support is limited

### Summary

The model weights ARE publicly available under Apache 2.0, making fine-tuning legally and technically feasible. However, there is currently NO official or community fine-tuning guide. The model is implemented in HuggingFace Transformers with custom code, so standard HF training approaches should be adaptable, but this would require non-trivial engineering effort to set up the data pipeline and training configuration from scratch. The community is actively requesting fine-tuning support (HF discussion #9) but Cohere has not yet responded.

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status: completed

prompt:
Research the Cohere Transcribe model released in March 2026. I need to understand:

1. First, fetch this blog post: https://huggingface.co/blog/CohereLabs/cohere-transcribe-03-2026-release — extract all details about the model architecture, weights availability, license, and any mention of fine-tuning.

2. Search the web for: "Cohere Transcribe fine-tune" OR "Cohere Transcribe finetune" — any guides, recipes, or discussions.

3. Search the web for: "CohereLabs/cohere-transcribe" huggingface model card — check if the model weights are publicly available on HuggingFace.

4. Search the web for: "Cohere Transcribe conformer fine-tuning" — since it's conformer-based, check if there are any conformer fine-tuning recipes that could apply.

5. Search for the actual HuggingFace model page: https://huggingface.co/CohereLabs — check what models they have published and whether weights are downloadable.

6. Search for any GitHub repos, issues, or discussions about fine-tuning this model.

7. Check if Cohere provides any fine-tuning API or documentation for their speech models.

Report back ALL findings in detail — especially:
- Are the model weights publicly available for download?
- What is the license?
- Is there any official or community fine-tuning guide?
- What framework does it use (NeMo, HuggingFace Transformers, custom)?
- Any blockers to fine-tuning?

agentType: general-purpose


text:
## Cohere Transcribe Fine-Tuning Research — Summary

### Can it be fine-tuned? **Technically yes, but no one has done it yet.**

Here's the full picture:

### Model Details
- **Architecture:** Fast-Conformer encoder (~90% params) + lightweight X-attention Transformer decoder
- **Size:** 2B parameters
- **Framework:** HuggingFace Transformers (custom `CohereAsrForConditionalGeneration` class) — **not NeMo**
- **Weights:** Publicly available at `CohereLabs/cohere-transcribe-03-2026`
- **License:** Apache 2.0 (fully permissive, commercial OK)

### Fine-Tuning Status: No guides exist
- **Zero official fine-tuning documentation** — blog post, model card, and README only cover inference
- **HuggingFace Discussion #9** ("Fine tuning code availability?") was opened 5 days ago with 9 upvotes — **no response from Cohere yet**
- **No community fine-tuning attempts published** — model is only ~1 week old
- Cohere's fine-tuning API/dashboard is for text LLMs only, not speech
- NeMo conformer recipes **do NOT directly apply** since Cohere reimplemented the architecture in pure HF Transformers

### What would fine-tuning require (DIY)?

Since it's a standard HF encoder-decoder model with weights in safetensors:

1. **HF `Seq2SeqTrainer`** should theoretically work — the model inherits from standard HF base classes
2. **LoRA/PEFT** is likely applicable (standard PyTorch modules underneath)
3. **Data pipeline** would need to be reverse-engineered from `processing_cohere_asr.py` — audio → 16kHz mono → log-Mel spectrogram → model input
4. **Language must be specified** at inference (no auto-detect), likely needed during training too

### Blockers / Risks
| Issue | Severity |
|---|---|
| No reference training config or hyperparameters | High |
| No training data collator published | Medium |
| 2B params → full fine-tune needs 40-80GB+ VRAM | High (LoRA mitigates) |
| Custom processor/tokenizer may have undocumented training quirks | Medium |
| Nobody has validated this works yet | High |

### Bottom Line

The model **can theoretically be fine-tuned** — weights are open, license is permissive, and the HF Transformers framework supports training. But you'd be the **first to attempt it**, with no recipe to follow. The practical path would be:

1. Read `modeling_cohere_asr.py` and `processing_cohere_asr.py` from the model repo
2. Set up a `Seq2SeqTrainer` with LoRA targeting the encoder's conformer layers
3. Build a data collator that mimics the processor's inference pipeline
4. Start with a small dataset to validate the training loop works

Want me to pull down the model's custom code files and start drafting a fine-tuning script?


type: last-prompt

lastPrompt: https://huggingface.co/blog/CohereLabs/cohere-transcribe-03-2026-release can we perform research that this model can be finetuned ? like can this be finetuned and can this be even attempted. cause thi…