def auto_run(directory):
for file in os.listdir(directory):
if not file.endswith('.json'):
continue
file_path = os.path.join(directory, file)
with open(file_path, 'r', encoding='utf-8') as f:
result = json_format.Parse(
f.read(),
types.cloud_speech_pb2.LongRunningRecognizeResponse())
format_transcript(
result.results, file_path[:-5]
) # output .srt formatted version of transcription
print(file_path)
def transcribe_gcs(mp4_file):
audio_file_path = process_video(mp4_file) #Create audio file
if audio_file_path:
bucket_name = 'jomi-speech-to-text.appspot.com' # Your gcloud bucket name
print(mp4_file)
audio_file_name = os.path.basename(audio_file_path) + '.ogg'
print(audio_file_name)
upload_to_gcloud(bucket_name,
source_file_name=audio_file_path + '.ogg',
destination_blob_name=audio_file_name)
"""Asynchronously transcribes the audio file specified by the gcs_uri."""
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri="gs://" + bucket_name + "/" +
audio_file_name)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.OGG_OPUS,
language_code='en-US',
sample_rate_hertz=16000,
enable_word_time_offsets=True)
operation = client.long_running_recognize(config, audio)
if not operation.done():
print('Waiting for results...')
result = operation.result()
results = result.results
raw_text_file = open(audio_file_path + '.txt', 'w')
for result in results:
for alternative in result.alternatives:
raw_text_file.write(
alternative.transcript.encode('utf-8') + '\n')
raw_text_file.close() #output raw text file of transcription
format_transcript(
results,
audio_file_path) #output .srt formatted version of transcription
else:
return
