--- title: "Wav2Vec2-Base-960h" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/29706acd-2079-4db5-a54d-6436b4514cb3" updated: "2026-11-13" --- # Wav2Vec2-Base-960h > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns English speech audio sampled at 16 kHz into text. A base wav2vec 2.0 model fine-tuned on 960 hours of Librispeech, for developers and researchers who need a transcription baseline. Wav2Vec2-Base-960h is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by facebook ## About ### What it is Wav2Vec2-Base-960h is a speech-to-text model for English. It reads an audio clip and writes out the words. Facebook pretrained the base model on speech audio and then fine-tuned it on 960 hours of Librispeech. ### Who it suits It suits developers and researchers who need English transcription or a baseline to compare against. People have published 181 fine-tuned versions of it. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add English transcription to an app | A script that transcribes audio files | | Research | Speech researchers | Use it as a baseline and measure word error rate | Scores on the clean and other LibriSpeech test sets | | Education | Students of speech recognition | Study how a speech model turns audio into text | Run the card's sample on a small dataset | If your audio is not English, you can skip it. ### Where to use it - Python on Windows, macOS or Linux - The transformers library, with PyTorch - A GPU, if you run the card's evaluation script, which sends the model to CUDA - Any audio source, as long as it is sampled at 16 kHz Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Transcribing audio files | Developers | Text decoded from the audio clip | | Measuring accuracy | Researchers | A word error rate on LibriSpeech test data | ### How to set it up - Python on Windows, macOS or Linux - The transformers, datasets and torch packages, which the card's code imports - Speech input sampled at 16 kHz 1. Install the packages the code imports. 2. Optionally download the model files by hand. 3. Run the usage code, which loads the model by its name. ```sh pip install -U huggingface_hub hf download facebook/wav2vec2-base-960h ``` ### How to use it Load the processor and the model, turn the audio array into input values, take the most likely token at each step, and decode the ids into text. The card shows this on a small sample dataset. ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` Pieces of the code | Tool | What it does | | --- | --- | | Wav2Vec2Processor | Turns the audio array into input values for the model | | Wav2Vec2ForCTC | Runs the model and returns scores for each step | | torch.argmax | Picks the most likely token at every step | | batch\_decode | Turns the chosen token ids into text | ### Example You load the first clip of the card's small sample dataset and run the code above. The model reads the audio and gives back its transcription as a text string, which you can compare with the clip's reference text. ### For newcomers and experts If you are new, think of this as a program that listens to a recording and types what it hears. The first small thing to try is the sample code on the one clip from the card's dataset. If you know the field, wav2vec 2.0 pretrains by masking parts of the audio features, then solving a contrastive task on quantised latent values. This checkpoint is used as a standalone acoustic model with CTC and no language model. The trade-off is accuracy on harder audio, with a word error rate of 3.4 on clean and 8.6 on other. Audio sampled at 16 kHz → Processor makes input values → Wav2Vec2ForCTC returns logits → Argmax picks token ids → batch\_decode writes the text ### Good to know Word error rate on LibriSpeech test data | Label | Value | | --- | --- | | clean | 3.4% | | other | 8.6% | - Input: the audio must be sampled at 16 kHz. - Language: the record lists English only. - Size: 94.4M parameters, about 378 MB in F32. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: it downloads the model and a sample dataset from Hugging Face, and the card names no other access. - Not for: speech in languages other than English. ## README ````markdown # Wav2Vec2-Base-960h Turns English speech audio sampled at 16 kHz into text. A base wav2vec 2.0 model fine-tuned on 960 hours of Librispeech, for developers and researchers who need a transcription baseline. Made by facebook on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Wav2Vec2-Base-960h is a speech-to-text model for English. It reads an audio clip and writes out the words. Facebook pretrained the base model on speech audio and then fine-tuned it on 960 hours of Librispeech. Who it suits: It suits developers and researchers who need English transcription or a baseline to compare against. People have published 181 fine-tuned versions of it. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Software | Developers | Add English transcription to an app | A script that transcribes audio files Research | Speech researchers | Use it as a baseline and measure word error rate | Scores on the clean and other LibriSpeech test sets Education | Students of speech recognition | Study how a speech model turns audio into text | Run the card's sample on a small dataset ``` If your audio is not English, you can skip it. Where to use it: - Python on Windows, macOS or Linux - The transformers library, with PyTorch - A GPU, if you run the card's evaluation script, which sends the model to CUDA - Any audio source, as long as it is sampled at 16 kHz ```table Title: Jobs it does Use case | Who | What they get back Transcribing audio files | Developers | Text decoded from the audio clip Measuring accuracy | Researchers | A word error rate on LibriSpeech test data ``` How to set it up: - Python on Windows, macOS or Linux - The transformers, datasets and torch packages, which the card's code imports - Speech input sampled at 16 kHz 1. Install the packages the code imports. 2. Optionally download the model files by hand. 3. Run the usage code, which loads the model by its name. ```sh pip install -U huggingface_hub hf download facebook/wav2vec2-base-960h ``` How to use it: Load the processor and the model, turn the audio array into input values, take the most likely token at each step, and decode the ids into text. The card shows this on a small sample dataset. ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ```table Title: Pieces of the code Tool | What it does Wav2Vec2Processor | Turns the audio array into input values for the model Wav2Vec2ForCTC | Runs the model and returns scores for each step torch.argmax | Picks the most likely token at every step batch_decode | Turns the chosen token ids into text ``` Example: You load the first clip of the card's small sample dataset and run the code above. The model reads the audio and gives back its transcription as a text string, which you can compare with the clip's reference text. For newcomers and experts: If you are new, think of this as a program that listens to a recording and types what it hears. The first small thing to try is the sample code on the one clip from the card's dataset. If you know the field, wav2vec 2.0 pretrains by masking parts of the audio features, then solving a contrastive task on quantised latent values. This checkpoint is used as a standalone acoustic model with CTC and no language model. The trade-off is accuracy on harder audio, with a word error rate of 3.4 on clean and 8.6 on other. ```flow Audio sampled at 16 kHz Processor makes input values Wav2Vec2ForCTC returns logits Argmax picks token ids batch_decode writes the text ``` Good to know: ```chart Title: Word error rate on LibriSpeech test data clean: 3.4% other: 8.6% ``` - Input: the audio must be sampled at 16 kHz. - Language: the record lists English only. - Size: 94.4M parameters, about 378 MB in F32. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: it downloads the model and a sample dataset from Hugging Face, and the card names no other access. - Not for: speech in languages other than English. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: transformers - Parameters: 94.4M - Weights: about 378 MB in F32; about 47 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,794,378 - Likes: 418 - License: apache-2.0 - Languages: en - Quantized versions: 11 - Fine-tunes: 181 - Tags: transformers, pytorch, safetensors, wav2vec2, automatic-speech-recognition, audio, hf-asr-leaderboard, model-index ## Links - Model: https://huggingface.co/facebook/wav2vec2-base-960h - Files: https://huggingface.co/facebook/wav2vec2-base-960h/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download facebook/wav2vec2-base-960h ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [facebook/wav2vec2-base-960h on Hugging Face](https://huggingface.co/facebook/wav2vec2-base-960h) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Wav2Vec2-Base-960h (this page) | 1,794,378 | 418 | 94.4M | apache-2.0 | 2022-11-14 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [ARK-ASR-3B](https://mindket.com/p/c33d475a-2543-47ca-93db-67b7dd111d35) | 1,655 | 115 | 4.1B | apache-2.0 | 2026-06-27 | | [whisper-persian-v4](https://mindket.com/p/e05b0e6b-d638-4f8e-a01f-88dd76669fc5) | 1,071 | 21 | 1.5B | apache-2.0 | 2025-06-03 | | [whisper-large-v3-turbo](https://mindket.com/p/0a437e3c-069c-48a1-b07a-428a0416bd9c) | 4,969,987 | 3,524 | 809M | mit | 2024-10-04 | | [Distil-Whisper distil-large-v3](https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31) | 754,155 | 386 | 756M | mit | 2026-04-21 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · fxtentacle · 2022-03-30](https://news.ycombinator.com/item?id=30853346): Real Time Speech Recognition with Gradio: The title is very misleading. This is a thin 10-line Gradio GUI in front of the Huggingface Pipeline API, the latter of which will download 1000+ python files, a professionally pre-trained 1GB asr model, and a 500MB language model. But to all of that, Gradio isn't contributing. They are merely the… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-11-14 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open facebook/wav2vec2-base-960h on Hugging Face](https://huggingface.co/facebook/wav2vec2-base-960h) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/29706acd-2079-4db5-a54d-6436b4514cb3) - [Its page on Mindket](https://mindket.com/p/29706acd-2079-4db5-a54d-6436b4514cb3)