Google 雲端語音轉文字
我最近試用了Google Cloud的語音轉文字API。以下是我用來執行轉錄的Python函數。
import os
import argparse
from google.cloud import storage
from google.cloud.speech_v2 import SpeechClient
from google.cloud.speech_v2.types import cloud_speech
MAX_AUDIO_LENGTH_SECS = 8 * 60 * 60
OUTPUT_DIRECTORY = "assets/transcriptions"
def run_batch_recognize(audio_gcs_uri, output_gcs_folder, language_code="en-US"):
"""
使用Google Cloud語音轉文字批量API轉錄音頻文件。
參數：
audio_gcs_uri: 音頻文件的GCS URI。
output_gcs_folder: 存儲轉錄的GCS文件夾URI。
language_code: 轉錄的語言代碼（例如，"en-US", "cmn-CN"）。
"""
client = SpeechClient()
config = cloud_speech.RecognitionConfig(
auto_decoding_config={},
features=cloud_speech.RecognitionFeatures(
enable_word_confidence=True,
enable_word_time_offsets=True,
),
model="long",
language_codes=[language_code],
)
output_config = cloud_speech.RecognitionOutputConfig(
gcs_output_config=cloud_speech.GcsOutputConfig(uri=output_gcs_folder),
)
files = [cloud_speech.BatchRecognizeFileMetadata(uri=audio_gcs_uri)]
request = cloud_speech.BatchRecognizeRequest(
recognizer="projects/graphite-ally-445108-k3/locations/global/recognizers/_",
config=config,
files=files,
recognition_output_config=output_config,
)
operation = client.batch_recognize(request=request)
print("等待操作完成...")
response = operation.result(timeout=3 * MAX_AUDIO_LENGTH_SECS)
print(response)
def process_audio_files(input_dir, output_dir):
os.makedirs(output_dir, exist_ok=True)
all_audio_files = [f for f in os.listdir(input_dir) if f.endswith(('.mp3', '.wav', '.m4a'))]
total_files = len(all_audio_files)
print(f"總共需要處理的音頻文件：{total_files}")
if total_files == 0:
print(f"在'{input_dir}'目錄中未找到音頻文件。")
return
files_processed = 0
for filename in all_audio_files:
audio_file_path = os.path.join(input_dir, filename)
output_filename = os.path.join(output_dir, f"{os.path.splitext(filename)[0]}.txt")
if os.path.exists(output_filename):
print(f"跳過 {filename}: {output_filename} 已存在。")
continue
print(f"\n處理 {files_processed + 1}/{total_files}: {filename}")
try:
# 根據文件名後綴確定語言
if filename.endswith('-zh.mp3') or filename.endswith('-zh.wav') or filename.endswith('-zh.m4a'):
language_code = "cmn-CN"
else:
language_code = "en-US"
# 構建GCS URI
gcs_audio_uri = f"gs://test2x/audio-files/{filename}" # 替換為你的存儲桶和文件夾
gcs_output_uri = f"gs://test2x/transcripts/{os.path.splitext(filename)[0]}" # 替換為你的存儲桶和文件夾
# 如果文件不存在，則上傳到GCS
# 這部分未實現，你需要添加代碼將文件上傳到GCS
# 例如，使用google-cloud-storage庫
storage_client = storage.Client()
bucket = storage_client.bucket("test2x")
blob = bucket.blob(f"audio-files/{filename}")
if not blob.exists():
blob.upload_from_filename(audio_file_path)
print(f"已上傳 {filename} 到GCS。")
else:
print(f"{filename} 已存在於GCS。")
run_batch_recognize(
audio_gcs_uri=gcs_audio_uri,
output_gcs_folder=gcs_output_uri,
language_code=language_code
)
files_processed += 1
print(f"文件 {files_processed}/{total_files} 已處理。\n")
# 下載轉錄
output_gcs_uri_json = f"{gcs_output_uri}/{os.path.splitext(filename)[0]}_transcript_*.json"
blobs = storage_client.list_blobs("test2x", prefix=f"transcripts/{os.path.splitext(filename)[0]}")
for blob in blobs:
if blob.name.endswith(".json"):
local_output_path = os.path.join(output_dir, os.path.basename(blob.name))
blob.download_to_filename(local_output_path)
print(f"已下載 {blob.name} 到 {local_output_path}")
except Exception as e:
print(f"處理 {filename} 失敗: {e}")
continue
print(f"處理完成！ {files_processed}/{total_files} 文件已處理。")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="處理音頻文件以生成轉錄。")
parser.add_argument('--input_dir', type=str, default="assets/audios", help="音頻文件的輸入目錄。")
args = parser.parse_args()
process_audio_files(
input_dir=args.input_dir,
output_dir=OUTPUT_DIRECTORY,
)
結果。修改後顯示JSON輸出的樣本，為簡潔起見刪除了一些值。完整的JSON將包含更詳細的信息。
{
"results": [
{
"alternatives": [
{
"transcript": "Here's To The Crazy Ones The Misfits the Rebels the troublemakers the round pegs in the square holes the ones who see things differently they're not fond of rules and they have no respect for the status quo",
"confidence": 0.95684826,
"words": [
{
"startOffset": "1s",
"endOffset": "4.200s",
"word": "Here's",
"confidence": 0.8265989
},
{
"startOffset": "4.200s",
"endOffset": "4.400s",
"word": "To",
"confidence": 0.9994259
},
{
"startOffset": "4.400s",
"endOffset": "4.400s",
"word": "The",
"confidence": 0.9994259
},
{
"startOffset": "4.400s",
"endOffset": "4.900s",
"word": "Crazy",
"confidence": 0.9975712
},
{
"startOffset": "4.900s",
"endOffset": "5.100s",
"word": "Ones",
"confidence": 0.9904002
},
{
"startOffset": "5.100s",
"endOffset": "6.700s",
"word": "The",
"confidence": 0.9994307
},
{
"startOffset": "6.700s",
"endOffset": "7.200s",
"word": "Misfits",
"confidence": 0.9352707
},
{
"startOffset": "7.200s",
"endOffset": "8.400s",
"word": "the",
"confidence": 0.99881697
},
{
"startOffset": "8.400s",
"endOffset": "8.800s",
"word": "Rebels",
"confidence": 0.9900544
},
{
"startOffset": "8.800s",
"endOffset": "10.300s",
"word": "the",
"confidence": 0.99904335
}
]
}
],
"resultEndOffset": "23.780s",
"languageCode": "en-us"
}
]
}