Whisper-NER

• Demo: https://huggingface.co/spaces/aiola/whisper-ner-v1
• Peper: WhisperNER: Unified Open Named Entity and Speech Recognition.
• Code: https://github.com/aiola-lab/whisper-ner

We introduce WhisperNER, a novel model that allows joint speech transcription and entity recognition. WhisperNER supports open-type NER, enabling recognition of diverse and evolving entities at inference. The WhisperNER model is designed as a strong base model for the downstream task of ASR with NER, and can be fine-tuned on specific datasets for improved performance.

NOTE: This model also support entity masking directly on the output transcript, especially relevant for PII use cases. However, the model was not trained on PII specific datasets, hence can perform general and open type entity masking, but it should be further funetuned in order to be used for PII tasks.

Training Details

aiola/whisper-ner-tag-and-mask-v1 was finetuned from aiola/whisper-ner-v1 using the NuNER dataset to perform joint audio transcription and NER tagging or NER masking. The model was trained and evaluated only on English data. Check out the paper for full details.

Usage

Inference can be done using the following code (for inference code and more details check out the whisper-ner repo).:

import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

model_path = "aiola/whisper-ner-tag-and-mask-v1"
audio_file_path = "path/to/audio/file"
prompt = "person, company, location"  # comma separated entity tags
apply_entity_mask = False  # change to True for entity masking
mask_token = "<|mask|>"

if apply_entity_mask:
    prompt = f"{mask_token}{prompt}"
    
# load model and processor from pre-trained
processor = WhisperProcessor.from_pretrained(model_path)
model = WhisperForConditionalGeneration.from_pretrained(model_path)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# load audio file: user is responsible for loading the audio files themselves
target_sample_rate = 16000
signal, sampling_rate = torchaudio.load(audio_file_path)
resampler = torchaudio.transforms.Resample(sampling_rate, target_sample_rate)
signal = resampler(signal)
# convert to mono or remove first dim if needed
if signal.ndim == 2:
    signal = torch.mean(signal, dim=0)
# pre-process to get the input features
input_features = processor(
    signal, sampling_rate=target_sample_rate, return_tensors="pt"
).input_features
input_features = input_features.to(device)

prompt_ids = processor.get_prompt_ids(prompt.lower(), return_tensors="pt")
prompt_ids = prompt_ids.to(device)

# generate token ids by running model forward sequentially
with torch.no_grad():
    predicted_ids = model.generate(
        input_features,
        prompt_ids=prompt_ids,
        generation_config=model.generation_config,
        language="en",
    )

# post-process token ids to text, remove prompt
transcription = processor.batch_decode(
    predicted_ids, skip_special_tokens=True
)[0]
print(transcription)