---
license: apache-2.0
language: de
library_name: transformers
thumbnail: null
tags:
- automatic-speech-recognition
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Fine-tuned whisper-large-v2 model for ASR in German
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: de
      split: test
      args: de
    metrics:
    - name: WER (Greedy)
      type: wer
      value: 5.76
---

<style>
img {
 display: inline;
}
</style>

![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey)
![Model size](https://img.shields.io/badge/Params-1550M-lightgrey)
![Language](https://img.shields.io/badge/Language-German-lightgrey)

# Fine-tuned whisper-large-v2 model for ASR in German

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co./openai/whisper-large-v2), trained on the mozilla-foundation/common_voice_11_0 de dataset. When using the model make sure that your speech input is also sampled at 16Khz. **This model also predicts casing and punctuation.**

## Performance

*Below are the WERs of the pre-trained models on the [Common Voice 9.0](https://huggingface.co./datasets/mozilla-foundation/common_voice_9_0). These results are reported in the original [paper](https://cdn.openai.com/papers/whisper.pdf).*

| Model | Common Voice 9.0 |
| --- | :---: |
| [openai/whisper-small](https://huggingface.co./openai/whisper-small) | 13.0 |
| [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) | 8.5 |
| [openai/whisper-large-v2](https://huggingface.co./openai/whisper-large-v2) | 6.4 |

*Below are the WERs of the fine-tuned models on the [Common Voice 11.0](https://huggingface.co./datasets/mozilla-foundation/common_voice_11_0).*

| Model | Common Voice 11.0 |
| --- | :---: |
| [bofenghuang/whisper-small-cv11-german](https://huggingface.co./bofenghuang/whisper-small-cv11-german) | 11.35 |
| [bofenghuang/whisper-medium-cv11-german](https://huggingface.co./bofenghuang/whisper-medium-cv11-german) | 7.05 |
| [bofenghuang/whisper-large-v2-cv11-german](https://huggingface.co./bofenghuang/whisper-large-v2-cv11-german) | **5.76** |

## Usage

Inference with 🤗 Pipeline

```python
import torch

from datasets import load_dataset
from transformers import pipeline

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load pipeline
pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-cv11-german", device=device)

# NB: set forced_decoder_ids for generation utils
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="de", task="transcribe")

# Load data
ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "de", split="test", streaming=True)
test_segment = next(iter(ds_mcv_test))
waveform = test_segment["audio"]

# NB: decoding option
# limit the maximum number of generated tokens to 225
pipe.model.config.max_length = 225 + 1
# sampling
# pipe.model.config.do_sample = True
# beam search
# pipe.model.config.num_beams = 5
# return
# pipe.model.config.return_dict_in_generate = True
# pipe.model.config.output_scores = True
# pipe.model.config.num_return_sequences = 5

# Run
generated_sentences = pipe(waveform)["text"]
```

Inference with 🤗 low-level APIs

```python
import torch
import torchaudio

from datasets import load_dataset
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load model
model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-cv11-german").to(device)
processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-cv11-german", language="german", task="transcribe")

# NB: set forced_decoder_ids for generation utils
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="de", task="transcribe")

# 16_000
model_sample_rate = processor.feature_extractor.sampling_rate

# Load data
ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "de", split="test", streaming=True)
test_segment = next(iter(ds_mcv_test))
waveform = torch.from_numpy(test_segment["audio"]["array"])
sample_rate = test_segment["audio"]["sampling_rate"]

# Resample
if sample_rate != model_sample_rate:
    resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate)
    waveform = resampler(waveform)

# Get feat
inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt")
input_features = inputs.input_features
input_features = input_features.to(device)

# Generate
generated_ids = model.generate(inputs=input_features, max_new_tokens=225)  # greedy
# generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5)  # beam search

# Detokenize
generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

# Normalise predicted sentences if necessary
```