diff options
Diffstat (limited to 'agl_service_voiceagent/utils/stt_model.py')
| -rw-r--r-- | agl_service_voiceagent/utils/stt_model.py | 105 |
1 files changed, 105 insertions, 0 deletions
diff --git a/agl_service_voiceagent/utils/stt_model.py b/agl_service_voiceagent/utils/stt_model.py new file mode 100644 index 0000000..5337162 --- /dev/null +++ b/agl_service_voiceagent/utils/stt_model.py @@ -0,0 +1,105 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (c) 2023 Malik Talha +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import json +import vosk +import wave +from agl_service_voiceagent.utils.common import generate_unique_uuid + +class STTModel: + def __init__(self, model_path, sample_rate=16000): + self.sample_rate = sample_rate + self.model = vosk.Model(model_path) + self.recognizer = {} + self.chunk_size = 1024 + + def setup_recognizer(self): + uuid = generate_unique_uuid(6) + self.recognizer[uuid] = vosk.KaldiRecognizer(self.model, self.sample_rate) + return uuid + + def init_recognition(self, uuid, audio_data): + return self.recognizer[uuid].AcceptWaveform(audio_data) + + def recognize(self, uuid, partial=False): + self.recognizer[uuid].SetWords(True) + if partial: + result = json.loads(self.recognizer[uuid].PartialResult()) + else: + result = json.loads(self.recognizer[uuid].Result()) + self.recognizer[uuid].Reset() + return result + + def recognize_from_file(self, uuid, filename): + if not os.path.exists(filename): + print(f"Audio file '{filename}' not found.") + return "FILE_NOT_FOUND" + + wf = wave.open(filename, "rb") + if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE": + print("Audio file must be WAV format mono PCM.") + return "FILE_FORMAT_INVALID" + + # audio_data = wf.readframes(wf.getnframes()) + # we need to perform chunking as target AGL system can't handle an entire audio file + audio_data = b"" + while True: + chunk = wf.readframes(self.chunk_size) + if not chunk: + break # End of file reached + audio_data += chunk + + if audio_data: + if self.init_recognition(uuid, audio_data): + result = self.recognize(uuid) + return result['text'] + else: + result = self.recognize(uuid, partial=True) + return result['partial'] + + else: + print("Voice not recognized. Please speak again...") + return "VOICE_NOT_RECOGNIZED" + + def cleanup_recognizer(self, uuid): + del self.recognizer[uuid] + +import wave + +def read_wav_file(filename, chunk_size=1024): + try: + wf = wave.open(filename, "rb") + if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE": + print("Audio file must be WAV format mono PCM.") + return "FILE_FORMAT_INVALID" + + audio_data = b"" # Initialize an empty bytes object to store audio data + while True: + chunk = wf.readframes(chunk_size) + if not chunk: + break # End of file reached + audio_data += chunk + + return audio_data + except Exception as e: + print(f"Error reading audio file: {e}") + return None + +# Example usage: +filename = "your_audio.wav" +audio_data = read_wav_file(filename) +
\ No newline at end of file |