Automatic Speech Recognition

Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text. It has many applications, such as voice user interfaces.

Automatic Speech Recognition Model

Going along slushy country roads and speaking to damp audiences in...

About Automatic Speech Recognition

Use Cases

Virtual Speech Assistants

Many edge devices have an embedded virtual assistant to interact with the end users better. These assistances rely on ASR models to recognize different voice commands to perform various tasks. For instance, you can ask your phone for dialing a phone number, ask a general question, or schedule a meeting.

Caption Generation

A caption generation model takes audio as input from sources to generate automatic captions through transcription, for live-streamed or recorded videos. This can help with content accessibility. For example, an audience watching a video that includes a non-native language, can rely on captions to interpret the content. It can also help with information retention at online-classes environments improving knowledge assimilation while reading and taking notes faster.

Task Variants

Multilingual ASR

Multilingual ASR models can convert audio inputs with multiple languages into transcripts. Some multilingual ASR models include language identification blocks to improve the performance.

The use of Multilingual ASR has become popular, the idea of maintaining just a single model for all language can simplify the production pipeline. Take a look at this model to get an idea on how 56 languages can be processed by a single model.


The Hub contains over 500 ASR models that you can use right away by trying out the widgets directly in the browser or calling the models as a service using the Inference API. Here is a simple code snippet to do exactly this:

import json
import requests

headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://api-inference.huggingface.co/models/facebook/wav2vec2-base-960h"

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.request("POST", API_URL, headers=headers, data=data)
    return json.loads(response.content.decode("utf-8"))

data = query("sample1.flac")

You can also use libraries such as transformers, speechbrain and espnet if you want to handle the Inference directly.

from transformers import pipeline

with open("sample.flac", "rb") as f:
  data = f.read()

pipe = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")

Solving ASR for your own data

We have some great news! You can do fine-tuning (transfer learning) to train a well-performing model without requiring as much data. Pretrained models such as Wav2Vec2 and HuBERT exist. Facebook's Wav2Vec2 XLS-R model is a large multilingual model trained on 128 languages and with 436K hours of speech.

The following detailed blog post shows how to fine-tune a pre-trained network on labeled data for ASR. This is easily done by adding a single layer on top of the pretrained network. We suggest to read the article for more info!

Hugging Face XLSR-Wav2Vec2 Sprint

On March 2020, over 300 participants collaborated, trained and shared 236 ASR models in dozens of different languages. You can compare these models thanks to the PapersWithCode integration (see Portuguese models for example).

Leaderboard of ASR Models

These events help democratize ASR for all languages, including low-resource languages. In addition to the trained models, the event helps to build practical collaborative knowledge.

Useful Resources

Automatic Speech Recognition demo
or or
This model can be loaded on the Inference API on-demand.
Models for Automatic Speech Recognition
Browse Models (7,173)

Note An end-to-end model that performs Automatic Speech Recognition and Speech Translation.

Datasets for Automatic Speech Recognition
Browse Datasets (162)

Note An English dataset with 1,000 hours of data.

Note Dataset in 60 languages including demographic information.

Note High quality, multi-speaker audio data and their transcriptions in various languages.

Spaces using Automatic Speech Recognition

Note A powerful general-purpose speech recognition application.

Note An application that transcribes speeches in YouTube videos.

Metrics for Automatic Speech Recognition
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system. The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort. This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate. Word error rate can then be computed as: WER = (S + D + I) / N = (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 words, N is the number of words in the reference (N=S+D+C). This value indicates the average number of errors per reference word. The lower the value, the better the performance of the ASR system with a WER of 0 being a perfect score.
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system. CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information. Character error rate can be computed as: CER = (S + D + I) / N = (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). CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to 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.