I’m having trouble getting proper transcription results when connecting Twilio phone call audio to OpenAI Whisper. My current setup keeps returning the same generic response instead of actual speech transcription.
Here’s my current implementation:
@websocket.route('/audio-feed')
def handle_audio(connection):
while True:
data = connection.receive()
parsed_data = json.loads(data)
if parsed_data['type'] == 'audio':
# decode base64 audio data
raw_audio = base64.b64decode(parsed_data['audio']['data'])
append_to_buffer(raw_audio)
audio_buffer = np.array([], dtype=np.float32)
def append_to_buffer(raw_audio):
global audio_buffer
# process raw audio into numpy format
processed = np.frombuffer(raw_audio, np.int16).flatten().astype(np.float32) / 32768.0
audio_buffer = np.concatenate((audio_buffer, processed))
transcribe_buffer()
def transcribe_buffer():
global audio_buffer
padded_audio = whisper.pad_or_trim(audio_buffer)
spectrogram = whisper.log_mel_spectrogram(padded_audio).to(whisper_model.device)
cleaned_spec = torch.nan_to_num(spectrogram)
transcription = whisper.decode(whisper_model, cleaned_spec, decode_options)
The issue is that instead of getting actual speech-to-text results, I always get the same placeholder text. Has anyone successfully implemented real-time Twilio to Whisper transcription? What might be causing this consistent output problem?
check your decode_options - if whisper’s temperature is too high, it’ll generate repetitive text instead of actual transcription. also make sure you’re clearing the audio_buffer after processing, otherwise old audio will mess with new chunks.
I encountered a similar issue while developing my own call transcription system. It seems that you are calling transcribe_buffer() too frequently, which leads to processing incomplete audio segments. Instead of transcribing every audio chunk, implement a delay or batch the audio data for a few seconds before triggering the transcription. Additionally, confirm that your audio’s sample rate is set to 16kHz since Whisper requires it, while Twilio defaults to 8kHz. This adjustment helped me achieve accurate transcriptions.
u might be sending small audio chunks to Whisper. try batching 2-3 secs of audio before calling transcribe_buffer(). also, double check if twilio’s sample rate is 16kHz like whisper needs, not 8kHz. that could be throwing things off!
You’re bypassing Whisper’s preprocessing pipeline, and that’s causing your issues. When you manually create spectrograms with whisper.log_mel_spectrogram(), you skip the normalization steps that whisper.transcribe() handles automatically. I hit this exact problem optimizing my real-time pipeline. Those generic responses happen because your manually processed spectrograms don’t match what the model expects. Don’t build spectrograms yourself - save your buffered audio as a temp WAV file and use whisper.transcribe() directly. Yeah, it’s slower because of file I/O, but transcription accuracy gets way better. Also check your decode_options config. Missing language hints or wrong settings make Whisper default to repetitive patterns.
Others already covered the audio format stuff, but honestly? Managing all these streaming connections and audio processing manually is a total nightmare. I’ve been through similar real-time transcription projects - the complexity just snowballs.
You need a proper automation platform that handles WebSocket connections, audio buffering, format conversions, and API calls without the headache. I switched to Latenode for these integrations since it’s got built-in connectors for Twilio and OpenAI.
With Latenode, you set up the whole flow visually. It handles Twilio audio streaming, buffers the right amount of data, converts formats, and sends chunks to Whisper at perfect intervals. No more wrestling with numpy arrays and sample rate hell.
Best part? You can add error handling, retry logic, and save transcriptions to your database without writing any code. Way more reliable than juggling all these pieces yourself.
The generic responses happen because Whisper’s voice detection struggles with bad audio quality. You’re processing every chunk as it comes in, but Twilio’s packets are usually too short and fragmented for decent transcription. I encountered a similar issue while building my own system. The solution was to implement proper silence detection before running transcription. Buffer the audio until you detect a natural pause, then transcribe the entire segment. Additionally, ensure that your WebSocket connections are stable, as packet loss during call volume spikes can lead to missed frames that make Whisper revert to generic outputs. It’s crucial to monitor connection stability and incorporate robust reconnection handling.
The problem is Twilio sends audio in μ-law format at 8kHz, but your code expects linear PCM. You need to decode the μ-law audio first before converting it to a numpy array. Also, make sure you’re collecting enough audio data - Whisper struggles with really short clips. I use a sliding window approach where I collect at least 1-2 seconds of audio before transcribing, and I resample from 8kHz to 16kHz using scipy.signal.resample. This made transcription much more accurate. If you don’t decode the μ-law properly, the audio gets distorted, which is why you’re getting those generic responses.
Been down this rabbit hole before. The issue isn’t just your audio processing - it’s trying to maintain real-time performance while juggling all these moving parts.
Your code shows the classic problem with manual Twilio-Whisper integration. You’re dealing with WebSocket management, audio buffering, format conversion, timing issues, and API rate limits all at once. One small hiccup anywhere breaks the whole chain.
I used to build these integrations from scratch too. Spent weeks debugging similar audio pipeline issues. Then I realized I was reinventing the wheel badly.
Smart move is using an automation platform that already solved these problems. Latenode has native Twilio and OpenAI integrations that handle the streaming, buffering, and format conversion automatically.
With Latenode, you drag and drop components to build your flow. Twilio audio comes in, gets properly buffered and formatted, then sent to Whisper at optimal intervals. No more numpy arrays, sample rate conversion, or WebSocket connection management.
You can also add real-time processing like sentiment analysis, keyword detection, or database storage without writing extra code. The visual workflow makes debugging way easier than tracking down issues in streaming audio code.
Saves you weeks of development time and gives you better reliability than a custom solution.
make sure to reset your audio_buffer after every transcription. adding audio_buffer = np.array([], dtype=np.float32) at the end of transcribe_buffer() will clear it out. otherwise, whisper could get overwhelmed by too much audio data.