---
title: Char Error Rate
description: >-
  Character Error Rate (CER) is a metric of the performance of an automatic
  speech recognition (ASR) system.
canonical: 'https://lightning.ai/docs/torchmetrics/stable/text/char_error_rate'
---
# Char Error Rate {#char-error-rate}

## Module Interface {#module-interface}

### `class torchmetrics.text.CharErrorRate(**kwargs)` {#torchmetrics.text.CharErrorRate}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/text/cer.py#L29-L140)

Character Error Rate ([CER](https://rechtsprechung-im-ostseeraum.archiv.uni-greifswald.de/word-error-rate-character-error-rate-how-to-evaluate-a-model)) is a metric of the performance of an automatic speech recognition (ASR) system.

This value indicates the percentage of characters that were incorrectly predicted. The lower the value, the better the performance of the ASR system with a CharErrorRate of 0 being a perfect score. Character error rate can then be computed as:

```math
CharErrorRate = \frac{S + D + I}{N} = \frac{S + D + I}{S + D + C}
```

where:

*   $`S`$ is the number of substitutions,
    
*   $`D`$ is the number of deletions,
    
*   $`I`$ is the number of insertions,
    
*   $`C`$ is the number of correct characters,
    
*   $`N`$ is the number of characters in the reference (N=S+D+C).
    

Compute CharErrorRate score of transcribed segments against references.

As input to `forward` and `update` the metric accepts the following input:

*   `preds` ([`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)")): Transcription(s) to score as a string or list of strings
    
*   `target` ([`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)")): Reference(s) for each speech input as a string or list of strings
    

As output of `forward` and `compute` the metric returns the following output:

*   `cer` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): A tensor with the Character Error Rate score
    

Parameters:

**kwargs** ([`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")) – Additional keyword arguments, see [Advanced metric settings](/docs/torchmetrics/1.9.0/user-guide/overview.md#metric-kwargs) for more info.

Examples

```python
>>> from torchmetrics.text import CharErrorRate
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> cer = CharErrorRate()
>>> cer(preds, target)
tensor(0.3415)
```

#### `plot(val=None, ax=None)` {#torchmetrics.text.CharErrorRate.plot}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/text/cer.py#L98-L140)

Plot a single or multiple values from the metric.

Parameters:

*   **val** (`Union`\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
    
*   **ax** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)")\]) – An matplotlib axis object. If provided will add plot to that axis
    

Return type:

[`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`Figure`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure "(in Matplotlib v3.11.2)"), `Union`\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)"), [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.5)")\]\]

Returns:

Figure and Axes object

Raises:

[**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If matplotlib is not installed

```python
>>> # Example plotting a single value
>>> from torchmetrics.text import CharErrorRate
>>> metric = CharErrorRate()
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
```

[![../../\_images/char\_error\_rate-1.png](/docs/torchmetrics/1.9.0/static/_images/char_error_rate-1.png)](/docs/torchmetrics/1.9.0/static/_images/char_error_rate-1.png)

```python
>>> # Example plotting multiple values
>>> from torchmetrics.text import CharErrorRate
>>> metric = CharErrorRate()
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
```

[![../../\_images/char\_error\_rate-2.png](/docs/torchmetrics/1.9.0/static/_images/char_error_rate-2.png)](/docs/torchmetrics/1.9.0/static/_images/char_error_rate-2.png)

## Functional Interface {#functional-interface}

### `torchmetrics.functional.text.char_error_rate(preds, target)` {#torchmetrics.functional.text.char_error_rate}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/functional/text/cer.py#L66-L87)

Compute Character Error Rate used for performance of an automatic speech recognition system.

This value indicates the percentage of characters that were incorrectly predicted. The lower the value, the better the performance of the ASR system with a CER of 0 being a perfect score.

Parameters:

*   **preds** (`Union`\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)"), [`list`](https://docs.python.org/3/builtins/stdtypes.html#list "(in Python v3.15)")\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)")\]\]) – Transcription(s) to score as a string or list of strings
    
*   **target** (`Union`\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)"), [`list`](https://docs.python.org/3/builtins/stdtypes.html#list "(in Python v3.15)")\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)")\]\]) – Reference(s) for each speech input as a string or list of strings
    

Return type:

[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")

Returns:

Character error rate score

Examples

```python
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> char_error_rate(preds=preds, target=target)
tensor(0.3415)
```

---

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