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安裝與設置

要開始使用Google Speech-to-Text API，首先需要在Python環境中安裝google-cloud-speech包，并在Google Cloud項目中啟用Speech-to-Text API。

%pip install --upgrade --quiet google-cloud-speech

按照Google Cloud快速入門指南創建項目并啟用API。

使用Google Speech-to-Text API

基本使用方法

使用Google Speech-to-Text API前，需要準備project_id和file_path。音頻文件可以是Google Cloud Storage的URI或本地文件路徑。

from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types

client = speech.SpeechClient()

file_path = 'gs://cloud-samples-data/speech/brooklyn_bridge.raw'
audio = types.RecognitionAudio(uri=file_path)
config = types.RecognitionConfig(
    encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    language_code='en-US',
)

response = client.recognize(config=config, audio=audio)

for result in response.results:
    print('Transcript: {}'.format(result.alternatives[0].transcript))

進階配置

可以通過config參數自定義識別配置，如選擇不同的語音識別模型和功能。

自定義識別配置

config = types.RecognitionConfig(
    encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    language_code='en-US',
    enable_automatic_punctuation=True,
)

常見問題與解決方案

網絡訪問問題

在某些地區訪問Google API可能會不穩定，推薦使用API代理服務提高訪問穩定性。例如，可以使用API代理服務。

音頻文件過長

Google Speech-to-Text API對單個音頻文件的長度有限制（60秒或10MB）。對于更長的音頻文件，可以將其分割成多個小文件進行處理。

語言支持問題

確保config中的language_code與音頻文件中的語言一致，以獲得最佳的識別效果。

實踐案例分析

語音文件轉文本Python示例

以下是一個使用Python將語音文件轉換為文本的完整示例。

from google.cloud import speech
client = speech.SpeechClient()

gcs_uri = 'gs://cloud-samples-data/speech/brooklyn_bridge.raw'
audio = speech.RecognitionAudio(uri=gcs_uri)
config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    language_code='en-US',
)

response = client.recognize(config=config, audio=audio)
for result in response.results:
    print('Transcript: {}'.format(result.alternatives[0].transcript))

麥克風語音轉文本Python示例

以下示例展示了如何使用麥克風實時捕捉語音并轉換為文本。

import os
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
import pyaudio
from six.moves import queue

os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'your-path-to-credentials.json'

RATE = 16000
CHUNK = int(RATE / 10)

class MicrophoneStream(object):
    def __init__(self, rate, chunk):
        self._rate = rate
        self._chunk = chunk
        self._buff = queue.Queue()
        self.closed = True

    def __enter__(self):
        self._audio_interface = pyaudio.PyAudio()
        self._audio_stream = self._audio_interface.open(
            format=pyaudio.paInt16,
            channels=1,
            rate=self._rate,
            input=True,
            frames_per_buffer=self._chunk,
            stream_callback=self._fill_buffer,
        )

        self.closed = False
        return self

    def __exit__(self, type, value, traceback):
        self._audio_stream.stop_stream()
        self._audio_stream.close()
        self.closed = True
        self._buff.put(None)
        self._audio_interface.terminate()

    def _fill_buffer(self, in_data, frame_count, time_info, status_flags):
        self._buff.put(in_data)
        return None, pyaudio.paContinue

    def generator(self):
        while not self.closed:
            chunk = self._buff.get()
            if chunk is None:
                return
            data = [chunk]
            try:
                while True:
                    chunk = self._buff.get(block=False)
                    if chunk is None:
                        return
                    data.append(chunk)
            except queue.Empty:
                break
            yield b''.join(data)

def listen_print_loop(responses):
    num_chars_printed = 0
    for response in responses:
        if not response.results:
            continue
        result = response.results[0]
        if not result.alternatives:
            continue
        transcript = result.alternatives[0].transcript
        overwrite_chars = ' ' * (num_chars_printed - len(transcript))
        if not result.is_final:
            sys.stdout.write(transcript + overwrite_chars + 'r')
            sys.stdout.flush()
            num_chars_printed = len(transcript)
        else:
            print(transcript + overwrite_chars)
            if re.search(r'b(exit|quit)b', transcript, re.I):
                print('Exiting..')
                break
            num_chars_printed = 0

def main():
    language_code = 'zh'
    client = speech.SpeechClient()
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=RATE,
        language_code=language_code,
    )
    streaming_config = speech.StreamingRecognitionConfig(
        config=config, interim_results=True
    )

    with MicrophoneStream(RATE, CHUNK) as stream:
        audio_generator = stream.generator()
        requests = (
            speech.StreamingRecognizeRequest(audio_content=content)
            for content in audio_generator
        )

        responses = client.streaming_recognize(streaming_config, requests)

        listen_print_loop(responses)

if __name__ == '__main__':
    main()