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---
license: mit
pipeline_tag: automatic-speech-recognition
---
# Whisper tflite models for use in Whisper app on F-Droid
"transcribe-translate" models provide signatures for "serving_transcribe" and "serving_translate" to force the model to perform a certain action
@tf.function(
input_signature=[
tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"),
],
)
def transcribe(self, input_features):
outputs = self.model.generate(
input_features,
max_new_tokens=450, # change as needed
return_dict_in_generate=True,
forced_decoder_ids=[[2, 50359], [3, 50363]], # forced to transcribe any language with no timestamps
)
return {"sequences": outputs["sequences"]}
@tf.function(
input_signature=[
tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"),
],
)
def translate(self, input_features):
outputs = self.model.generate(
input_features,
max_new_tokens=450, # change as needed
return_dict_in_generate=True,
forced_decoder_ids=[[2, 50358], [3, 50363]], # forced to translate any language with no timestamps
)
return {"sequences": outputs["sequences"]}
In order to force transcription for a certain language set the 1. decoder id as shown below:
def transcribe(self, input_features):
outputs = self.model.generate(
input_features,
max_new_tokens=450, # change as needed
return_dict_in_generate=True,
forced_decoder_ids=[[1, 50261], [2, 50359], [3, 50363]], # forced to transcribe (50359) German (50261) with no timestamps (50363)
)
return {"sequences": outputs["sequences"]}
def translate(self, input_features):
outputs = self.model.generate(
input_features,
max_new_tokens=450, # change as needed
return_dict_in_generate=True,
forced_decoder_ids=[[1, 50261], [2, 50358], [3, 50363]], # different forced_decoder_ids
)
return {"sequences": outputs["sequences"]}
(language codes from here: https://github.com/woheller69/whisperIME/blob/master/app/src/main/java/com/whispertflite/utils/InputLang.java) |