# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-04 01:17:57
- **Session ID**: `0bc8e60b-ba83-4a58-a04a-a6c3891c8026`

## Conversation

### 👤 You

Skipping row due to error: 'torchcodec.decoders.AudioDecoder' object is not subscriptable
Skipping row due to error: 'torchcodec.decoders.AudioDecoder' object is not subscriptable
Skipping row due to error: 'torchcodec.decoders.AudioDecoder' object is not subscriptable
Map: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1195/1195 [00:09<00:00, 124.00 examples/s]
Filter: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1195/1195 [00:00<00:00, 328431.51 examples/s]
*** HERE you can modify the text prompt
If you are training a multi-speaker model (e.g., canopylabs/orpheus-3b-0.1-ft),
ensure that the dataset includes a "source" field and format the input accordingly:
- Single-speaker: f"{example['text']}"
- Multi-speaker: f"{example['source']}: {example['text']}"

GPU = NVIDIA H200 NVL. Max memory = 139.801 GB.
6.615 GB of memory reserved.
Traceback (most recent call last):
  File "/root/orpheus.py", line 267, in <module>
    trainer_stats = trainer.train()
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/transformers/trainer.py", line 2328, in train
    return inner_training_loop(
        args=args,
    ...<2 lines>...
        ignore_keys_for_eval=ignore_keys_for_eval,
    )
  File "<string>", line 23, in _fast_inner_training_loop
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/transformers/trainer.py", line 1143, in get_train_dataloader
    return self._get_dataloader(
           ~~~~~~~~~~~~~~~~~~~~^
        dataset=self.train_dataset,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<3 lines>...
        is_training=True,
        ^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/transformers/trainer.py", line 1098, in _get_dataloader
    dataset = self._remove_unused_columns(dataset, description=description)
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/transformers/trainer.py", line 1024, in _remove_unused_columns
    raise ValueError(
    ...<3 lines>...
    )
ValueError: No columns in the dataset match the model's forward method signature: (input_ids, causal_mask, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, num_logits_to_keep, logits_to_keep, args, kwargs, labels, label_ids, label). The following columns have been ignored: []. Please check the dataset and model. You may need to set `remove_unused_columns=False` in `TrainingArguments`.

---

### 🤖 Cursor Assistant

I can see the issue - the dataset preprocessing is failing, and as a result, no columns are being created that match what the model expects. Let me examine the current code to understand what's happening.

---

### 🤖 Cursor Assistant

Looking at your error, there are two main issues:

1. **AudioDecoder errors during preprocessing**: Some audio samples are failing to process with the `'torchcodec.decoders.AudioDecoder' object is not subscriptable` error
2. **Missing columns for training**: The trainer can't find the expected columns (`input_ids`, `labels`, `attention_mask`) in the dataset

The second issue is critical. The problem is that the `Trainer` is initialized without a proper data collator and without setting `remove_unused_columns=False`. Let me fix this:

---

### 🤖 Cursor Assistant

Now let me add some debugging information to help verify the dataset columns are correct:

---

### 🤖 Cursor Assistant

Now let me also improve the audio processing error handling to better diagnose the AudioDecoder issue:

---

### 🤖 Cursor Assistant

Perfect! The linter errors are just warnings about unresolved imports, which is expected since these packages need to be installed in your training environment.

## Summary of Fixes

I've made three key changes to fix your training error:

### 1. **Added `remove_unused_columns=False` to TrainingArguments** (Line 267)
This tells the Trainer to keep all dataset columns instead of trying to automatically match them to the model signature. This was the main cause of your error.

### 2. **Added a proper DataCollator** (Lines 242-247)
The `DataCollatorForSeq2Seq` will handle batching and padding correctly for your sequence data, ensuring the `input_ids`, `labels`, and `attention_mask` are properly formatted for each batch.

### 3. **Improved audio preprocessing error handling** (Lines 131-159)
Added better handling for different audio format types to reduce the AudioDecoder errors. The code now handles:
   - Dictionary format with "array" key (original)
   - Tensor-like objects with `.numpy()` method
   - Array-like objects with `__array__()` method
   - Direct numpy array conversion as fallback

### 4. **Added debug output** (Lines 232-241)
Added diagnostic prints to show dataset information before training, so you can verify the columns are correctly set up.

The main issue was that without `remove_unused_columns=False` and without a proper data collator, the Trainer was trying to automatically match columns to the model signature and failing. Now it will use your custom columns directly.

Try running your training script again - it should now work! The debug output will help you confirm that the dataset has the correct structure before training begins.

---

### 👤 You

| 249/1195 [00:02<00:07, 123.78 examples/s]
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
Skipping row due to error: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.

---

### 🤖 Cursor Assistant

Good progress! Now we have a different error - the audio arrays are of type `numpy.object_` which can't be converted to torch tensors. This means the audio data needs to be explicitly converted to a numeric dtype before processing. Let me fix this:

---

### 🤖 Cursor Assistant

Perfect! I've fixed the audio array dtype issue. The changes now include:

## Updated Fix

**Enhanced dtype handling in `add_codes` function** (Lines 154-160):
- Detects when audio arrays have dtype `numpy.object_` 
- Explicitly converts them to `np.float32` which torch can handle
- Also ensures any other non-float types are converted to `float32`

This should resolve the `"can't convert np.ndarray of type numpy.object_"` error. The audio data in the dataset appears to be stored as object arrays, which need explicit conversion to a numeric type before torch can create tensors from them.

Try running the script again - it should now successfully process the audio arrays and create the training dataset! You'll still see some "Skipping row" messages for truly corrupted data, but the object dtype issue should be resolved.

---

### 👤 You

Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.
Skipping row due to error: setting an array element with a sequence.

---

### 🤖 Cursor Assistant

The error "setting an array element with a sequence" occurs when the object array contains nested sequences with inconsistent shapes that can't be directly converted. Let me add more robust handling for this case:

---

### 🤖 Cursor Assistant

Perfect! I've enhanced the audio processing to handle nested/jagged arrays. The updated code now:

## Key Improvements (Lines 154-182)

1. **Detects object arrays** and handles them specially
2. **Flattens nested structures**: Iterates through the object array and properly flattens any nested lists or arrays
3. **Converts each element**: Handles both individual numeric values and nested sequences
4. **Ensures 1D output**: Flattens multi-dimensional arrays to ensure compatibility with the audio tokenizer
5. **Better error handling**: Catches specific ValueError/TypeError and raises a clear message when conversion isn't possible

This should resolve the "setting an array element with a sequence" error by properly handling the nested array structures that some audio samples appear to have. The samples that truly can't be converted will still be skipped, but most should now process successfully.

Try running the script again - the audio preprocessing should now be much more robust!

---

### 👤 You

Skipping row due to error: Unable to convert object array to numeric format
Skipping row due to error: Unable to convert object array to numeric format

Did you see the dataset format?

---

### 🤖 Cursor Assistant

You're right - let me examine the actual dataset structure to understand what format the audio data is in. Let me check the dataset:

---

### 🤖 Cursor Assistant

Now let me also add some debugging in the `add_codes` function to see what's happening with the problematic samples:

---

### 🤖 Cursor Assistant

Perfect! I've added debugging code to inspect the dataset structure. When you run the script now, it will print out detailed information about:

1. **Dataset features and schema**
2. **Audio field structure** (keys, type, dtype, shape)
3. **Sample values** from the first audio array
4. **Text and source fields** if they exist

This will help us understand exactly what format the audio data is in and why some samples are failing to convert.

**Please run the script again and share the output from the "=== Dataset Structure ===" section.** This will show us the actual format of the audio data, and I can then adjust the `add_codes` function to handle it properly.

Note: I've also added a `debug_failed_samples` flag (line 154) that you can set to `True` if you want more detailed debugging information about which specific samples are failing and why.

---

### 👤 You

=== Dataset Structure ===
Dataset features: {'audio': Audio(sampling_rate=None, decode=True, stream_index=None), 'text': Value('string')}
Number of samples: 1195

First sample keys: dict_keys(['audio', 'text'])
First sample 'audio' type: <class 'datasets.features._torchcodec.AudioDecoder'>
First sample 'audio' keys: Not a dict
Text field: 'Please have mercy on my dainty, frail body. Your coils are so strong and powerful, and I am powerles...'
==================================================

Parameter 'function'=<function add_codes at 0x7776986b8f40> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only shown once. Subsequent hashing failures won't be shown.
Map:   0%|                                                                                                                                                                            | 0/1195 [00:00<?, ? examples/s]
  Debug - dtype: object, shape: ()
  Debug - first element type: <class 'datasets.features._torchcodec.AudioDecoder'>
  Debug - inner error: setting an array element with a sequence.
Skipping row due to error: Unable to convert object array to numeric format
  Debug - dtype: object, shape: ()
  Debug - first element type: <class 'datasets.features._torchcodec.AudioDecoder'>
  Debug - inner error: setting an array element with a sequence.
Skipping row due to error: Unable to convert object array to numeric format
  Debug - dtype: object, shape: ()
  Debug - first element type: <class 'datasets.features._torchcodec.AudioDecoder'>
  Debug - inner error: setting an array element with a sequence.
Skipping row due to error: Unable to convert object array to numeric format
  Debug - dtype: object, shape: ()
  Debug - first element type: <class 'datasets.features._torchcodec.AudioDecoder'>
  Debug - inner error: setting an array element with a sequence.

---

### 🤖 Cursor Assistant

Perfect! Now I can see the issue clearly. The audio data is coming as `datasets.features._torchcodec.AudioDecoder` objects, not as dictionaries. The dataset is using torchcodec but not automatically decoding. Let me fix the audio handling:

---

### 🤖 Cursor Assistant

Actually, let me also try a better approach - force the dataset to properly decode the audio upfront:

---

### 🤖 Cursor Assistant

Perfect! I've made two key improvements:

## Changes Made

1. **Added `Audio` import and forced audio decoding** (Lines 91, 102):
   - Imported the `Audio` class from datasets
   - Added `dataset.cast_column("audio", Audio(sampling_rate=None, decode=True))` to force the dataset to properly decode audio files into numpy arrays before processing

2. **Enhanced AudioDecoder handling** (Lines 151-221):
   - Added comprehensive handling for `AudioDecoder` objects as a fallback
   - Tries multiple methods to extract audio data: `.audio`, `.decode()`, `.data`, or iteration
   - Handles various formats that might be returned after decoding

The `cast_column` call should force the dataset to decode all audio properly before we start processing, which should eliminate the AudioDecoder objects. When you run the script now:

1. It will cast the audio column to ensure proper decoding
2. The debug output will show if the audio is now properly decoded as a dict with an 'array' key
3. The `add_codes` function will handle the properly decoded audio

**Please run the script again and share the output.** The audio should now be properly decoded into numpy arrays, and you should see much fewer (or no) "Skipping row" errors!

---

### 👤 You

Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.42it/s]
Unsloth 2025.9.11 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.

=== Dataset Structure ===
Dataset features: {'audio': Audio(sampling_rate=None, decode=True, stream_index=None), 'text': Value('string')}
Number of samples: 1195
Casting audio feature to ensure proper decoding...

First sample keys: dict_keys(['audio', 'text'])
First sample 'audio' type: <class 'datasets.features._torchcodec.AudioDecoder'>
First sample 'audio' keys: Not a dict
Text field: 'Please have mercy on my dainty, frail body. Your coils are so strong and powerful, and I am powerles...'
==================================================

Parameter 'function'=<function add_codes at 0x7035fd0651c0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only shown once. Subsequent hashing failures won't be shown.
Map:   0%|                                                                                                                                                                            | 0/1195 [00:00<?, ? examples/s]
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>

---

### 🤖 Cursor Assistant

The `cast_column` didn't work because the dataset is still using torchcodec. Let me try a different approach - we need to set the datasets backend to not use torchcodec, or manually load the audio:

---

### 🤖 Cursor Assistant

Actually, the environment variable needs to be set BEFORE importing datasets. Let me fix that:

---

### 🤖 Cursor Assistant

Perfect! I've made the critical fix - setting the audio backend to `soundfile` **before** importing the datasets library. Here's what changed:

## Key Fix

**Set audio backend at the top of the file** (Lines 58-60):
```python
import os
# Set audio backend BEFORE importing datasets to avoid torchcodec issues
os.environ["HF_DATASETS_AUDIO_BACKEND"] = "soundfile"
```

This is placed before all other imports (before `unsloth`, `torch`, and `datasets` imports) to ensure the datasets library uses the soundfile backend instead of torchcodec when loading audio.

## Why This Fixes the Issue

- **torchcodec**: Returns `AudioDecoder` objects that are difficult to work with and don't have standard numpy array access
- **soundfile**: Returns standard dictionaries with `{'array': numpy_array, 'sampling_rate': int}` format that your code expects

**Try running the script now!** The audio should load properly as numpy arrays in dictionary format, and you should see output like:
```
First sample 'audio' type: <class 'dict'>
First sample 'audio' keys: dict_keys(['array', 'sampling_rate', ...])
Audio array type: <class 'numpy.ndarray'>
Audio array dtype: float32
```

This should eliminate all the AudioDecoder errors and allow the training to proceed! 🎉

---

### 👤 You

Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.75it/s]
Unsloth 2025.9.11 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.

=== Dataset Structure ===
Dataset features: {'audio': Audio(sampling_rate=None, decode=True, stream_index=None), 'text': Value('string')}
Number of samples: 1195
Casting audio feature to ensure proper decoding with soundfile backend...

First sample keys: dict_keys(['audio', 'text'])
First sample 'audio' type: <class 'datasets.features._torchcodec.AudioDecoder'>
First sample 'audio' keys: Not a dict
Text field: 'Please have mercy on my dainty, frail body. Your coils are so strong and powerful, and I am powerles...'
==================================================

Parameter 'function'=<function add_codes at 0x740c84ae11c0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only shown once. Subsequent hashing failures won't be shown.
Map:   0%|                                                                                                                                                                            | 0/1195 [00:00<?, ? examples/s]
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Map:   1%|█▉                                                                                                                                                                | 14/1195 [00:00<00:09, 124.81 examples/s]
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>

--
still does not work. check how @sesame_csm_sft_unsloth.py works

---

### 🤖 Cursor Assistant

Looking at the sesame script, I can see the key difference! They cast the audio to a **specific sampling rate** (24000), not `None`. Let me update the orpheus script to match:

---

### 🤖 Cursor Assistant

Now let me also update the `ds_sample_rate` to use this new variable:

---

### 🤖 Cursor Assistant

Perfect! I've updated the script to match how the sesame script handles audio loading. Here are the key changes:

## Changes Made

1. **Set audio backend environment variable early** (Lines 58-60):
   ```python
   import os
   os.environ["HF_DATASETS_AUDIO_BACKEND"] = "soundfile"
   ```
   This is set before any imports to force the use of soundfile instead of torchcodec.

2. **Cast audio with specific sampling rate** (Lines 104-108):
   ```python
   target_sampling_rate = 24000
   dataset = dataset.cast_column("audio", Audio(sampling_rate=target_sampling_rate))
   ```
   The key was specifying an exact sampling rate (24000 Hz) instead of `None`. This forces the dataset to actually decode the audio into proper numpy arrays.

3. **Use the target sampling rate consistently** (Line 134):
   ```python
   ds_sample_rate = target_sampling_rate
   ```
   Instead of trying to get it from the dataset, we use the target rate we set.

**This matches exactly how the sesame script handles the same dataset!** (See sesame lines 113-114)

Try running the script now - the audio should be properly decoded as dictionaries with `'array'` and `'sampling_rate'` keys, and your preprocessing should work! 🎉

---

### 👤 You

Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.63it/s]
Unsloth 2025.9.11 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.

=== Dataset Structure ===
Dataset features: {'audio': Audio(sampling_rate=None, decode=True, stream_index=None), 'text': Value('string')}
Number of samples: 1195
Casting audio feature to 24000Hz...

First sample keys: dict_keys(['audio', 'text'])
First sample 'audio' type: <class 'datasets.features._torchcodec.AudioDecoder'>
First sample 'audio' keys: Not a dict
Text field: 'Please have mercy on my dainty, frail body. Your coils are so strong and powerful, and I am powerles...'
==================================================

Parameter 'function'=<function add_codes at 0x7a24a40a9120> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only shown once. Subsequent hashing failures won't be shown.
Map:   0%|                                                                                                                                                                            | 0/1195 [00:00<?, ? examples/s]
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Map:   1%|█▎                                                                                                                                                                 | 10/1195 [00:00<00:14, 81.41 examples/s]
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>
Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>

--
why the map still has problem? How did the working @sesame_csm_sft_unsloth.py work?

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### 🤖 Cursor Assistant

Looking at the sesame script more carefully, I notice they don't actually manually process the audio arrays themselves - they pass it directly to the processor's `apply_chat_template`. But in orpheus, you're manually tokenizing audio with SNAC.

The issue is that even after `cast_column`, the audio is still an `AudioDecoder` object. The environment variable might not be working because `datasets` may have already been imported by `unsloth`. Let me try a different approach - manually decode the audio in the map function:

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### 🤖 Cursor Assistant

Perfect! I've updated the code to **manually decode AudioDecoder objects using soundfile**. The key changes:

1. **Import soundfile in the function** (Line 172)
2. **Manually load audio from AudioDecoder** (Lines 176-189):
   - Check for `.path` attribute and use `sf.read()` to load the audio file
   - Check for `.bytes` attribute for in-memory audio
   - Check for `._path` as a fallback

This bypasses the automatic decoding mechanism and directly reads the audio files using soundfile, which should work regardless of the backend settings.

**Try running it now!** The function will manually decode each AudioDecoder object using soundfile, which should successfully extract the audio arrays.

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### 👤 You

Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>

--
Check the code on how to load it

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### 👤 You

Skipping row due to error: Cannot extract audio from AudioDecoder object: <class 'datasets.features._torchcodec.AudioDecoder'>

--
Check the online docs on how to load it

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*