Python

Model

class Model(model_path)[source]

Class holding a Coqui STT model

Parameters

aModelPath (str) – Path to model file to load

addHotWord(word, boost)[source]

Add a word and its boost for decoding.

Words that don’t occur in the scorer (e.g. proper nouns) or strings that contain spaces won’t be taken into account.

Parameters
  • word (str) – the hot-word

  • boost (float) – Positive boost value increases and negative reduces chance of a word occuring in a transcription. Excessive positive boost might lead to splitting up of letters of the word following the hot-word.

Throws

RuntimeError on error

beamWidth()[source]

Get beam width value used by the model. If setModelBeamWidth was not called before, will return the default value loaded from the model file.

Returns

Beam width value used by the model.

Type

int

clearHotWords()[source]

Remove all entries from hot-words dict.

Throws

RuntimeError on error

createStream()[source]

Create a new streaming inference state. The streaming state returned by this function can then be passed to feedAudioContent() and finishStream().

Returns

Stream object representing the newly created stream

Type

Stream()

Throws

RuntimeError on error

disableExternalScorer()[source]

Disable decoding using an external scorer.

Returns

Zero on success, non-zero on failure.

enableExternalScorer(scorer_path)[source]

Enable decoding using an external scorer.

Parameters

scorer_path (str) – The path to the external scorer file.

Throws

RuntimeError on error

eraseHotWord(word)[source]

Remove entry for word from hot-words dict.

Parameters

word (str) – the hot-word

Throws

RuntimeError on error

sampleRate()[source]

Return the sample rate expected by the model.

Returns

Sample rate.

Type

int

setBeamWidth(beam_width)[source]

Set beam width value used by the model.

Parameters

beam_width (int) – The beam width used by the model. A larger beam width value generates better results at the cost of decoding time.

Returns

Zero on success, non-zero on failure.

Type

int

setScorerAlphaBeta(alpha, beta)[source]

Set hyperparameters alpha and beta of the external scorer.

Parameters
  • alpha (float) – The alpha hyperparameter of the decoder. Language model weight.

  • beta (float) – The beta hyperparameter of the decoder. Word insertion weight.

Returns

Zero on success, non-zero on failure.

Type

int

stt(audio_buffer)[source]

Use the Coqui STT model to perform Speech-To-Text.

Parameters

audio_buffer (numpy.int16 array) – A 16-bit, mono raw audio signal at the appropriate sample rate (matching what the model was trained on).

Returns

The STT result.

Type

str

sttWithMetadata(audio_buffer, num_results=1)[source]

Use the Coqui STT model to perform Speech-To-Text and return results including metadata.

Parameters
  • audio_buffer (numpy.int16 array) – A 16-bit, mono raw audio signal at the appropriate sample rate (matching what the model was trained on).

  • num_results (int) – Maximum number of candidate transcripts to return. Returned list might be smaller than this.

Returns

Metadata object containing multiple candidate transcripts. Each transcript has per-token metadata including timing information.

Type

Metadata()

Stream

class Stream(native_stream)[source]

Class wrapping a stt stream. The constructor cannot be called directly. Use Model.createStream()

feedAudioContent(audio_buffer)[source]

Feed audio samples to an ongoing streaming inference.

Parameters

audio_buffer (numpy.int16 array) – A 16-bit, mono raw audio signal at the appropriate sample rate (matching what the model was trained on).

Throws

RuntimeError if the stream object is not valid

finishStream()[source]

Compute the final decoding of an ongoing streaming inference and return the result. Signals the end of an ongoing streaming inference. The underlying stream object must not be used after this method is called.

Returns

The STT result.

Type

str

Throws

RuntimeError if the stream object is not valid

finishStreamWithMetadata(num_results=1)[source]

Compute the final decoding of an ongoing streaming inference and return results including metadata. Signals the end of an ongoing streaming inference. The underlying stream object must not be used after this method is called.

Parameters

num_results (int) – Maximum number of candidate transcripts to return. Returned list might be smaller than this.

Returns

Metadata object containing multiple candidate transcripts. Each transcript has per-token metadata including timing information.

Type

Metadata()

Throws

RuntimeError if the stream object is not valid

freeStream()[source]

Destroy a streaming state without decoding the computed logits. This can be used if you no longer need the result of an ongoing streaming inference.

Throws

RuntimeError if the stream object is not valid

intermediateDecode()[source]

Compute the intermediate decoding of an ongoing streaming inference.

Returns

The STT intermediate result.

Type

str

Throws

RuntimeError if the stream object is not valid

intermediateDecodeWithMetadata(num_results=1)[source]

Compute the intermediate decoding of an ongoing streaming inference and return results including metadata.

Parameters

num_results (int) – Maximum number of candidate transcripts to return. Returned list might be smaller than this.

Returns

Metadata object containing multiple candidate transcripts. Each transcript has per-token metadata including timing information.

Type

Metadata()

Throws

RuntimeError if the stream object is not valid

Metadata

class Metadata[source]
transcripts()[source]

List of candidate transcripts

Returns

A list of CandidateTranscript() objects

Type

list

CandidateTranscript

class CandidateTranscript[source]

Stores the entire CTC output as an array of character metadata objects

confidence()[source]

Approximated confidence value for this transcription. This is roughly the sum of the acoustic model logit values for each timestep/character that contributed to the creation of this transcription.

tokens()[source]

List of tokens

Returns

A list of TokenMetadata() elements

Type

list

TokenMetadata

class TokenMetadata[source]

Stores each individual character, along with its timing information

start_time()[source]

Position of the token in seconds

text()[source]

The text for this token

timestep()[source]

Position of the token in units of 20ms