Whisper Timestamps and Temperature (#19509)
This PR updates exporting and running the Whisper model with beam search
by adding the following.
- Adds temperature as a graph input to the exported model
- Fixes the token ids by adding them as attributes to
`WhisperBeamSearch`
- Fixes the timestamps test cases so they pass now
- Fixes a bug with invoking `torch.onnx.export`
- Cleans up the Whisper scripts and groups the arguments in
`convert_to_onnx.py`
- Adds a `requirements.txt` file to specify package dependencies
- Adds `whisper-large-v3` to list of pretrained models
- Fixes a bug with missing cross-attention KV cache inputs in the
decoder subgraph
- This is a follow-up to [this
PR](https://github.com/microsoft/onnxruntime/pull/19188).
- The incorrect token ids in the timestamps processor were first noticed
during [this PR
review](https://github.com/microsoft/onnxruntime/pull/17500#discussion_r1333520007).
When they were originally added in [this
PR](https://github.com/microsoft/onnxruntime/pull/15853), the offsets
were previously constant across the Whisper model sizes. When comparing
the new `whisper-large-v3` variant, the English-only variants (e.g.
`whisper-tiny.en`), and the original variants (e.g. `whisper-tiny`),
both the values and the offsets differ. Therefore, it is easier to set
the token ids as attributes to `WhisperBeamSearch` when exporting to
ensure the right values are used in the timestamps processor.
- The Hugging Face API for returning timestamps and the expected outputs
from the PyTorch model have both changed.
- The fix for `torch.onnx.export` is a follow-up to [this PR
review](https://github.com/microsoft/onnxruntime/pull/17179#issuecomment-1683001470).
- The argument grouping is a follow-up to [this PR
review](https://github.com/microsoft/onnxruntime/pull/17500#discussion_r1333521721).
- Specific package versions are needed to run the Whisper scripts and
the `requirements.txt` file ensures that these versions are installed.
- The `whisper-large-v3` variant is released and should be in the list
of official pretrained models.
- After the changes from [this
PR](https://github.com/microsoft/onnxruntime/pull/17316), the exported
model is not loading in an ORT inference session because the
cross-attention KV cache inputs are missing in the decoder subgraph.
