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*This model was released on {release_date} and added to Hugging Face Transformers on 2025-12-05.*

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# LASR

## Overview

LASR is the architecture behind MedASR, a speech-to-text model from Google Health AI pre-trained for medical dictation. It's based on the [Conformer architecture](https://huggingface.co/papers/2005.08100) and designed as a starting point for developers building dictation tools with medical terminology, like radiology dictation. MedASR performs well on medical audio but can struggle with terms outside its training data, such as non-standard medication names or temporal references (dates, times, or durations).

## Usage

### Basic usage

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="google/medasr")
out = pipe("path/to/audio.mp3")
print(out)
```

</hfoption>
<hfoption id="AutoModel">

```py
from transformers import AutoModelForCTC, AutoProcessor
from datasets import load_dataset, Audio

processor = AutoProcessor.from_pretrained("google/medasr")
model = AutoModelForCTC.from_pretrained("google/medasr", dtype="auto", device_map="auto")

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el['array'] for el in ds["audio"][:5]]

inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate)
inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs)
print(processor.batch_decode(outputs))
```

</hfoption>
</hfoptions>

### Training

The example below prepares a batch of audio and text, passes it through the LASR/MedASR model, and computes the training loss.

```python
from transformers import AutoModelForCTC, AutoProcessor
from datasets import load_dataset, Audio

# Load processor and model
processor = AutoProcessor.from_pretrained("google/medasr")
model = AutoModelForCTC.from_pretrained("google/medasr", dtype="auto", device_map="auto")

# Load a small example dataset and prepare batch
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el["array"] for el in ds["audio"][:5]]
text_samples = [el for el in ds["text"][:5]]

# Passing `text` to the processor will prepare the `labels`
inputs = processor(audio=speech_samples, text=text_samples, sampling_rate=processor.feature_extractor.sampling_rate)
inputs.to(device, dtype=model.dtype)

outputs = model(**inputs)
outputs.loss.backward()
```

## LasrTokenizer

[[autodoc]] LasrTokenizer

## LasrFeatureExtractor

[[autodoc]] LasrFeatureExtractor
    - __call__

## LasrProcessor

[[autodoc]] LasrProcessor
    - __call__
    - batch_decode
    - decode

## LasrEncoderConfig

[[autodoc]] LasrEncoderConfig

## LasrCTCConfig

[[autodoc]] LasrCTCConfig

## LasrEncoder

[[autodoc]] LasrEncoder

## LasrForCTC

[[autodoc]] LasrForCTC
