Inference Providers

Automatic Speech Recognition

Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text.

Example applications:

• Transcribing a podcast
• Building a voice assistant
• Generating subtitles for a video

For more details about the automatic-speech-recognition task, check out its dedicated page! You will find examples and related materials.

Recommended models

• openai/whisper-large-v3: A powerful ASR model by OpenAI.

Explore all available models and find the one that suits you best here.

Using the API

API specification

Request

Headers
authorization	string	Authentication header in the form 'Bearer: hf_****' when hf_**** is a personal user access token with “Inference Providers” permission. You can generate one from your settings page.

Payload
inputs*	string	The input audio data as a base64-encoded string. If no parameters are provided, you can also provide the audio data as a raw bytes payload.
parameters	object
return_timestamps	boolean	Whether to output corresponding timestamps with the generated text
generation_parameters	object
temperature	number	The value used to modulate the next token probabilities.
top_k	integer	The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p	number	If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
typical_p	number	Local typicality measures how similar the conditional probability of predicting a target token next is to the expected conditional probability of predicting a random token next, given the partial text already generated. If set to float < 1, the smallest set of the most locally typical tokens with probabilities that add up to typical_p or higher are kept for generation. See this paper for more details.
epsilon_cutoff	number	If set to float strictly between 0 and 1, only tokens with a conditional probability greater than epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on the size of the model. See Truncation Sampling as Language Model Desmoothing for more details.
eta_cutoff	number	Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model. See Truncation Sampling as Language Model Desmoothing for more details.
max_length	integer	The maximum length (in tokens) of the generated text, including the input.
max_new_tokens	integer	The maximum number of tokens to generate. Takes precedence over max_length.
min_length	integer	The minimum length (in tokens) of the generated text, including the input.
min_new_tokens	integer	The minimum number of tokens to generate. Takes precedence over min_length.
do_sample	boolean	Whether to use sampling instead of greedy decoding when generating new tokens.
early_stopping	enum	Possible values: never, true, false.
num_beams	integer	Number of beams to use for beam search.
num_beam_groups	integer	Number of groups to divide num_beams into in order to ensure diversity among different groups of beams. See this paper for more details.
penalty_alpha	number	The value balances the model confidence and the degeneration penalty in contrastive search decoding.
use_cache	boolean	Whether the model should use the past last key/values attentions to speed up decoding

Response

Body
text	string	The recognized text.
chunks	object[]	When returnTimestamps is enabled, chunks contains a list of audio chunks identified by the model.
text	string	A chunk of text identified by the model
timestamp	number[]	The start and end timestamps corresponding with the text

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