# -*- coding: utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. # mypy: disable-error-code="annotation-unchecked" import json import os import threading import time import uuid from http import HTTPStatus from queue import Queue from threading import Timer from typing import Any, Dict, List, Union from dashscope.api_entities.dashscope_response import RecognitionResponse from dashscope.client.base_api import BaseApi from dashscope.common.constants import ApiProtocol from dashscope.common.error import ( InputDataRequired, InputRequired, InvalidParameter, InvalidTask, ModelRequired, ) from dashscope.common.logging import logger from dashscope.common.utils import _get_task_group_and_task from dashscope.protocol.websocket import WebsocketStreamingMode class RecognitionResult(RecognitionResponse): """The result set of speech recognition, including the single-sentence recognition result returned by the callback mode, and all recognition results in a synchronized manner. """ def __init__( self, response: RecognitionResponse, sentences: List[Any] = None, usages: List[Any] = None, ): self.status_code = response.status_code self.request_id = response.request_id self.code = response.code self.message = response.message self.usages = usages if sentences is not None and len(sentences) > 0: self.output = {"sentence": sentences} else: self.output = response.output if ( self.usages is not None and len( self.usages, ) > 0 and "usage" in self.usages[-1] ): self.usage = self.usages[-1]["usage"] else: self.usage = None def __str__(self): return json.dumps( RecognitionResponse.from_api_response(self), ensure_ascii=False, ) def get_sentence(self) -> Union[Dict[str, Any], List[Any]]: """The result of speech recognition.""" if self.output and "sentence" in self.output: return self.output["sentence"] else: return None # type: ignore[return-value] def get_request_id(self) -> str: """The request_id of speech recognition.""" return self.request_id def get_usage(self, sentence: Dict[str, Any]) -> Dict[str, Any]: """Get billing for the input sentence.""" if self.usages is not None: if ( sentence is not None and "end_time" in sentence and sentence["end_time"] is not None ): for usage in self.usages: if usage["end_time"] == sentence["end_time"]: return usage["usage"] return None # type: ignore[return-value] @staticmethod def is_sentence_end(sentence: Dict[str, Any]) -> bool: """Determine whether the speech recognition result is the end of a sentence. # noqa: E501 This is a static method. """ # pylint: disable=simplifiable-if-statement if ( sentence is not None and "end_time" in sentence and sentence["end_time"] is not None ): return True else: return False class RecognitionCallback: """An interface that defines callback methods for getting speech recognition results. # noqa E501 # pylint: disable=line-too-long Derive from this class and implement its function to provide your own data. """ def on_open(self) -> None: pass def on_complete(self) -> None: pass def on_error(self, result: RecognitionResult) -> None: pass def on_close(self) -> None: pass def on_event(self, result: RecognitionResult) -> None: pass class Recognition(BaseApi): """Speech recognition interface. Args: model (str): The requested model_id. callback (RecognitionCallback): A callback that returns speech recognition results. format (str): The input audio format for speech recognition. sample_rate (int): The input audio sample rate for speech recognition. workspace (str): The dashscope workspace id. **kwargs: phrase_id (list, `optional`): The ID of phrase. disfluency_removal_enabled(bool, `optional`): Filter mood words, turned off by default. diarization_enabled (bool, `optional`): Speech auto diarization, turned off by default. speaker_count (int, `optional`): The number of speakers. timestamp_alignment_enabled (bool, `optional`): Timestamp-alignment calibration, turned off by default. special_word_filter(str, `optional`): Sensitive word filter. audio_event_detection_enabled(bool, `optional`): Audio event detection, turned off by default. Raises: InputRequired: Input is required. """ SILENCE_TIMEOUT_S = 23 def __init__( self, model: str, callback: RecognitionCallback, format: str, # pylint: disable=redefined-builtin sample_rate: int, workspace: str = None, **kwargs, ): if model is None: raise ModelRequired("Model is required!") if format is None: raise InputRequired("format is required!") if sample_rate is None: raise InputRequired("sample_rate is required!") self.model = model self.format = format self.sample_rate = sample_rate # continuous recognition with start() or once recognition with call() self._recognition_once = False self._callback = callback self._running = False self._stream_data = Queue() self._worker = None self._silence_timer = None self._kwargs = kwargs self._workspace = workspace self._start_stream_timestamp = -1 self._first_package_timestamp = -1 self._stop_stream_timestamp = -1 self._on_complete_timestamp = -1 self.request_id_confirmed = False self.last_request_id = uuid.uuid4().hex def __del__(self): if self._running: self._running = False self._stream_data = Queue() if self._worker is not None and self._worker.is_alive(): self._worker.join() if ( self._silence_timer is not None and self._silence_timer.is_alive() # noqa E501 ): self._silence_timer.cancel() self._silence_timer = None if self._callback: self._callback.on_close() def __receive_worker(self): """Asynchronously, initiate a real-time speech recognition request and obtain the result for parsing. """ responses = self.__launch_request() for part in responses: # pylint: disable=R1702 if part.status_code == HTTPStatus.OK: if len(part.output) == 0 or ( "finished" in part.output # pylint: disable=singleton-comparison and part.output["finished"] == True # noqa: E712 ): self._on_complete_timestamp = time.time() * 1000 logger.debug( "last package delay %s", self.get_last_package_delay(), ) self._callback.on_complete() else: usage: Dict[str, Any] = None usages: List[Any] = None if "sentence" in part.output: if ( "text" in part.output["sentence"] and part.output["sentence"]["text"] != "" ): if self._first_package_timestamp < 0: self._first_package_timestamp = ( time.time() * 1000 ) logger.debug( "first package delay %s", self.get_first_package_delay(), ) sentence = part.output["sentence"] if ( "heartbeat" in sentence # pylint: disable=singleton-comparison and sentence["heartbeat"] == True # noqa: E712 ): logger.debug("recv heartbeat") continue logger.debug( "Recv Result [rid:%s]:%s, isEnd: %s", part.request_id, sentence, RecognitionResult.is_sentence_end(sentence), ) if part.usage is not None: usage = { "end_time": part.output["sentence"][ "end_time" ], "usage": part.usage, } usages = [usage] if ( self.request_id_confirmed is False and part.request_id is not None ): self.last_request_id = part.request_id self.request_id_confirmed = True self._callback.on_event( RecognitionResult( RecognitionResponse.from_api_response(part), usages=usages, ), ) else: self._running = False self._stream_data = Queue() self._callback.on_error( RecognitionResult( RecognitionResponse.from_api_response(part), ), ) self._callback.on_close() break def __launch_request(self): """Initiate real-time speech recognition requests.""" resources_list: list = [] if self._phrase is not None and len(self._phrase) > 0: item = {"resource_id": self._phrase, "resource_type": "asr_phrase"} resources_list.append(item) if len(resources_list) > 0: self._kwargs["resources"] = resources_list self._tidy_kwargs() task_name, _ = _get_task_group_and_task(__name__) responses = super().call( model=self.model, task_group="audio", task=task_name, function="recognition", input=self._input_stream_cycle(), api_protocol=ApiProtocol.WEBSOCKET, ws_stream_mode=WebsocketStreamingMode.DUPLEX, is_binary_input=True, sample_rate=self.sample_rate, format=self.format, stream=True, workspace=self._workspace, pre_task_id=self.last_request_id, **self._kwargs, ) return responses def start(self, phrase_id: str = None, **kwargs): """Real-time speech recognition in asynchronous mode. Please call 'stop()' after you have completed recognition. Args: phrase_id (str, `optional`): The ID of phrase. **kwargs: disfluency_removal_enabled(bool, `optional`): Filter mood words, turned off by default. diarization_enabled (bool, `optional`): Speech auto diarization, turned off by default. speaker_count (int, `optional`): The number of speakers. timestamp_alignment_enabled (bool, `optional`): Timestamp-alignment calibration, turned off by default. special_word_filter(str, `optional`): Sensitive word filter. audio_event_detection_enabled(bool, `optional`): Audio event detection, turned off by default. Raises: InvalidParameter: This interface cannot be called again if it has already been started. InvalidTask: Task create failed. """ assert ( self._callback is not None ), "Please set the callback to get the speech recognition result." # noqa E501 if self._running: raise InvalidParameter("Speech recognition has started.") self._start_stream_timestamp = -1 self._first_package_timestamp = -1 self._stop_stream_timestamp = -1 self._on_complete_timestamp = -1 self._phrase = phrase_id self._kwargs.update(**kwargs) self._recognition_once = False self._worker = threading.Thread(target=self.__receive_worker) self._worker.start() if self._worker.is_alive(): self._running = True self._callback.on_open() # If audio data is not received for 23 seconds, the timeout exits self._silence_timer = Timer( Recognition.SILENCE_TIMEOUT_S, self._silence_stop_timer, ) self._silence_timer.start() else: self._running = False raise InvalidTask("Invalid task, task create failed.") # pylint: disable=R1702,too-many-branches,too-many-statements def call( # type: ignore[override] # noqa: E501 self, file: str, phrase_id: str = None, **kwargs, ) -> RecognitionResult: """Real-time speech recognition in synchronous mode. Args: file (str): The path to the local audio file. phrase_id (str, `optional`): The ID of phrase. **kwargs: disfluency_removal_enabled(bool, `optional`): Filter mood words, turned off by default. diarization_enabled (bool, `optional`): Speech auto diarization, turned off by default. speaker_count (int, `optional`): The number of speakers. timestamp_alignment_enabled (bool, `optional`): Timestamp-alignment calibration, turned off by default. special_word_filter(str, `optional`): Sensitive word filter. audio_event_detection_enabled(bool, `optional`): Audio event detection, turned off by default. Raises: InvalidParameter: This interface cannot be called again if it has already been started. InputDataRequired: The supplied file was empty. Returns: RecognitionResult: The result of speech recognition. """ self._start_stream_timestamp = time.time() * 1000 if self._running: raise InvalidParameter("Speech recognition has been called.") if os.path.exists(file): if os.path.isdir(file): raise IsADirectoryError("Is a directory: " + file) else: raise FileNotFoundError("No such file or directory: " + file) self._recognition_once = True self._stream_data = Queue() self._phrase = phrase_id self._kwargs.update(**kwargs) error_flag: bool = False sentences: List[Any] = [] usages: List[Any] = [] response: RecognitionResponse = None result: RecognitionResult = None try: audio_data: bytes = None # pylint: disable=consider-using-with f = open(file, "rb") if os.path.getsize(file): while True: audio_data = f.read(12800) if not audio_data: break self._stream_data.put( audio_data, ) # pylint: disable=no-else-break else: raise InputDataRequired( "The supplied file was empty (zero bytes long)", ) f.close() self._stop_stream_timestamp = time.time() * 1000 except Exception as e: logger.error(e) raise e if not self._stream_data.empty(): self._running = True responses = self.__launch_request() for part in responses: if part.status_code == HTTPStatus.OK: if "sentence" in part.output: if ( "text" in part.output["sentence"] and part.output["sentence"]["text"] != "" ): if self._first_package_timestamp < 0: self._first_package_timestamp = ( time.time() * 1000 ) logger.debug( "first package delay %s", self._first_package_timestamp - self._start_stream_timestamp, ) sentence = part.output["sentence"] logger.debug( "Recv Result [rid:%s]:%s, isEnd: %s", part.request_id, sentence, RecognitionResult.is_sentence_end(sentence), ) if RecognitionResult.is_sentence_end(sentence): sentences.append(sentence) if part.usage is not None: usage = { "end_time": part.output["sentence"][ "end_time" ], "usage": part.usage, } usages.append(usage) response = RecognitionResponse.from_api_response(part) else: response = RecognitionResponse.from_api_response(part) logger.error(response) error_flag = True break self._on_complete_timestamp = time.time() * 1000 logger.debug( "last package delay %s", self.get_last_package_delay(), ) if error_flag: result = RecognitionResult(response) else: result = RecognitionResult(response, sentences, usages) self._stream_data = Queue() self._recognition_once = False self._running = False return result def stop(self): """End asynchronous speech recognition. Raises: InvalidParameter: Cannot stop an uninitiated recognition. """ if self._running is False: raise InvalidParameter("Speech recognition has stopped.") self._stop_stream_timestamp = time.time() * 1000 self._running = False if self._worker is not None and self._worker.is_alive(): self._worker.join() self._stream_data = Queue() if self._silence_timer is not None and self._silence_timer.is_alive(): self._silence_timer.cancel() self._silence_timer = None if self._callback: self._callback.on_close() def send_audio_frame(self, buffer: bytes): """Push speech recognition. Raises: InvalidParameter: Cannot send data to an uninitiated recognition. """ if self._running is False: raise InvalidParameter("Speech recognition has stopped.") if self._start_stream_timestamp < 0: self._start_stream_timestamp = time.time() * 1000 logger.debug("send_audio_frame: %s", len(buffer)) self._stream_data.put(buffer) def _tidy_kwargs(self): for k in self._kwargs.copy(): if self._kwargs[k] is None: self._kwargs.pop(k, None) def _input_stream_cycle(self): while self._running: while self._stream_data.empty(): if self._running: time.sleep(0.01) continue break # Reset silence_timer when getting stream. if ( self._silence_timer is not None and self._silence_timer.is_alive() # noqa E501 ): self._silence_timer.cancel() self._silence_timer = Timer( Recognition.SILENCE_TIMEOUT_S, self._silence_stop_timer, ) self._silence_timer.start() while not self._stream_data.empty(): frame = self._stream_data.get() yield bytes(frame) if self._recognition_once: self._running = False # drain all audio data when invoking stop(). if self._recognition_once is False: while not self._stream_data.empty(): frame = self._stream_data.get() yield bytes(frame) def _silence_stop_timer(self): """If audio data is not received for a long time, exit worker.""" self._running = False if self._silence_timer is not None and self._silence_timer.is_alive(): self._silence_timer.cancel() self._silence_timer = None if self._worker is not None and self._worker.is_alive(): self._worker.join() self._stream_data = Queue() def get_first_package_delay(self): """First Package Delay is the time between start sending audio and receive first words package""" # noqa: E501 # pylint: disable=line-too-long return self._first_package_timestamp - self._start_stream_timestamp def get_last_package_delay(self): """Last Package Delay is the time between stop sending audio and receive last words package""" # noqa: E501 # pylint: disable=line-too-long return self._on_complete_timestamp - self._stop_stream_timestamp # 获取上一个任务的taskId def get_last_request_id(self): return self.last_request_id