create demo

app.py

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+import datetime
+import os
+os.system('pip install git+https://github.com/openai/whisper.git')
+import gradio as gr
+import wave
+import whisper
+import logging
+import torchaudio
+import torchaudio.functional as F
+
+LOGGING_FORMAT = '%(asctime)s %(message)s'
+logging.basicConfig(format=LOGGING_FORMAT,level=logging.INFO)
+
+REC_INTERVAL_IN_SECONDS = 3
+
+# tmp dir to store audio files.
+if not os.path.isdir('./tmp/'):
+    os.mkdir('./tmp')
+
+class WhisperStreaming():
+    def __init__(self, model_name='base', language='en', fp16=False):
+        self.model_name = model_name
+        self.language = language
+        self.fp16 = fp16
+        self.whisper_model = whisper.load_model(f'{model_name}.{language}')
+        self.decode_option = whisper.DecodingOptions(language=self.language,
+                                                     without_timestamps=True,
+                                                     fp16=self.fp16)
+        self.whisper_sample_rate = 16000
+
+    def transcribe_audio_file(self, wave_file_path):
+        waveform, sample_rate = torchaudio.load(wave_file_path)
+        resampled_waveform = F.resample(waveform, sample_rate, self.whisper_sample_rate, lowpass_filter_width=6)
+        audio_tmp = whisper.pad_or_trim(resampled_waveform[0])
+        mel = whisper.log_mel_spectrogram(audio_tmp)
+        results = self.whisper_model.decode(mel, self.decode_option)
+        return results
+
+def concat_multiple_wav_files(wav_files):
+    logging.info(f'Concat {wav_files}')
+    concat_audio = []
+    for wav_file in wav_files:
+        w = wave.open(wav_file, 'rb')
+        concat_audio.append([w.getparams(), w.readframes(w.getnframes())])
+        w.close()
+        logging.info(f'Delete audio file {wav_file}')
+        os.remove(wav_file)
+
+    output_file_name = f'{datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f")}.wav'
+    output_file_path = os.path.join('./tmp', output_file_name)
+    output = wave.open(output_file_path, 'wb')
+    output.setparams(concat_audio[0][0])
+
+    for i in range(len(concat_audio)):
+        output.writeframes(concat_audio[i][1])
+    output.close()
+    logging.info(f'Concat past {len(wav_files)} wav files into {output_file_path}')
+    return output_file_path
+
+
+# fp16 indicates whether using Float16 or Float32. Normally, PyTorch does not support fp16 when run on CPU
+whisper_model = WhisperStreaming(model_name='base', language='en', fp16=False)
+
+
+def transcribe(audio, state={}):
+    logging.info(f'Transcribe audio file {audio}')
+    print('=====================')
+    logging.info(state)
+
+    if not state:
+        state['concated_audio'] = audio
+        state['result_text'] = 'Waitting...'
+        state['count'] = 0
+    else:
+        state['concated_audio'] = concat_multiple_wav_files([state['concated_audio'], audio])
+        state['count'] += 1
+
+    if state['count'] % REC_INTERVAL_IN_SECONDS == 0 and state['count'] > 0:
+        logging.info('start to transcribe.......')
+        result = whisper_model.transcribe_audio_file(state['concated_audio'])
+        logging.info('complete transcribe.......')
+        state['result_text'] = result.text
+        logging.info('The text is:' + state['result_text'])
+    else:
+        logging.info(f'The count of streaming is {state["count"]}, and skip speech recognition')
+
+    return state['result_text'], state
+
+
+gr.Interface(fn=transcribe,
+             inputs=[gr.Audio(source="microphone", type='filepath', streaming=True), 'state'],
+             outputs = ['text', 'state'],
+             live=True).launch()