openai/whisper-large-v3
openai/whisper-large-v3
2
1
Overview Code Discussions Pull Requests Wiki Activity

Speculative Decoding doesn't work yet with Whisper-v3 (#23)

Browse Source 
- Speculative Decoding doesn't work yet with Whisper-v3 (3264a14fc680cf148949b17860f045ce2cf81edb)
This commit is contained in:
Patrick von Platen 2023-11-16 11:43:26 +00:00 committed by system
parent cf8f9cff50
commit 77b8369da9
1 changed files with 0 additions and 51 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

51
README.md
View File

@ -258,57 +258,6 @@ result = pipe(sample, return_timestamps=True, generate_kwargs={"language": "fren
print(result["chunks"]) print(result["chunks"])
``` ```
## Speculative Decoding
Whisper `tiny` can be used as an assistant model to Whisper for speculative decoding. Speculative decoding mathematically
ensures the exact same outputs as Whisper are obtained while being 2 times faster. This makes it the perfect drop-in
replacement for existing Whisper pipelines, since the same outputs are guaranteed.
In the following code-snippet, we load the assistant Distil-Whisper model standalone to the main Whisper pipeline. We then
specify it as the "assistant model" for generation:
```python
from transformers import pipeline, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq, AutoProcessor
import torch
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
assistant_model_id = "openai/whisper-tiny"
assistant_model = AutoModelForCausalLM.from_pretrained(
assistant_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
assistant_model.to(device)
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
generate_kwargs={"assistant_model": assistant_model},
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
## Additional Speed & Memory Improvements ## Additional Speed & Memory Improvements
You can apply additional speed and memory improvements to Whisper-large-v3 which we cover in the following. You can apply additional speed and memory improvements to Whisper-large-v3 which we cover in the following.