--- 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)