With a Deepgram speech-to-speech agent, is there a way to interrogate the text the LLM generates before it goes to text-to-speech? I want to be able to detect and modify things in the speech freely.
#Interrogate text with python before TTS
pulsar jungle
pearl forumBOT
Hi @pulsar jungle. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
Based on the knowledge sources provided, there isn't a direct built-in way to interrogate and modify the text generated by an LLM before it goes to Deepgram's text-to-speech (TTS) service. However, you can implement this functionality in your Python code by intercepting the text before sending it to Deepgram's TTS API. Here's how you could approach this:
- Generate text with your LLM.
- Interrogate and modify the generated text as needed.
- Send the modified text to Deepgram's TTS API.
The Python SDK Streaming Text-to-Speech (https://developers.deepgram.com/docs/python-sdk-streaming-text-to-speech#make-a-deepgram-text-to-speech-request) documentation shows how to use Deepgram's WebSocket client for TTS. You can modify this example to include your text interrogation step:
from deepgram import DeepgramClient, DeepgramClientOptions
# Your text interrogation function
def interrogate_and_modify_text(text):
# Implement your text analysis and modification logic here
# For example, you could use regex, NLP libraries, or custom rules
modified_text = text # Replace this with your actual modification logic
return modified_text
# Your LLM-generated text
llm_text = "Hello, this is a text generated by an LLM."
# Interrogate and modify the text
modified_text = interrogate_and_modify_text(llm_text)
# Now use the modified text with Deepgram's TTS API
config = DeepgramClientOptions(options={"speaker_playback": "true"})
deepgram = DeepgramClient("YOUR_API_KEY", config)
dg_connection = deepgram.speak.websocket.v("1")
# ... (rest of the WebSocket setup code)
# Send the modified text to Deepgram
dg_connection.send_text(modified_text)
This approach allows you to freely detect and modify the text before it's sent to Deepgram's TTS service. You can implement any custom logic in the interrogate_and_modify_text function to suit your specific needs.
Remember that while this method gives you control over the text, it's not a built-in feature of Deepgram's API. You're responsible for implementing the text interrogation and modification logic in your Python code.
For more details on using Deepgram's TTS WebSocket API, you can refer to the Python SDK Streaming Text-to-Speech documentation (https://developers.deepgram.com/docs/python-sdk-streaming-text-to-speech#make-a-deepgram-text-to-speech-request).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
pulsar jungle
@pearl forum Will this result in a higher latency?
pearl forumBOT
Let me check the knowledge base for you again. One moment! :books:
Yes, interrogating and modifying the text before sending it to Deepgram's Text-to-Speech (TTS) service will likely result in higher latency. The additional processing step you're introducing between the LLM output and the TTS input will add some delay to the overall process.
According to the Text to Speech Latency (https://developers.deepgram.com/docs/text-to-speech-latency) documentation, there are several factors that contribute to TTS latency:
- Network latency
- Time to First Byte (TTFB)
- Audio synthesis latency
By adding a text interrogation step, you're introducing an additional source of latency before the text even reaches Deepgram's TTS service. The amount of added latency will depend on the complexity of your text interrogation and modification logic.
To minimize the impact on latency, you should consider the following tips:
- Optimize your text interrogation function to be as efficient as possible.
- Consider using asynchronous processing if applicable to your use case.
- Follow Deepgram's recommendations for minimizing TTS latency, such as:
- Chunking text inputs appropriately
- Using streaming when possible
- Optimizing the length of text input
As mentioned in the Text Chunking for TTS Optimization (https://developers.deepgram.com/docs/text-chunking-for-tts-streaming-optimization) guide, you should "try experimenting with different sentence chunk lengths (chunk before the first "," and ".") of the first sentence of your response to further optimize the latency of time-to-first-byte from an LLM output."
While the added text interrogation step will increase latency, careful optimization of both your custom logic and the way you interact with Deepgram's TTS service can help minimize the impact on the overall user experience.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
pulsar jungle
Does Deepgram support SSML? @pearl forum
pearl forumBOT
Let me check the knowledge base for you again. One moment! :books:
Based on the knowledge sources provided, Deepgram currently does not support SSML (Speech Synthesis Markup Language) for its Text-to-Speech (TTS) API. This information comes from an official response to a user question in the Deepgram GitHub discussions.
According to the SSML Tags discussion (https://github.com/orgs/deepgram/discussions/655), a Deepgram representative stated: "as of right now, we have no support for SSML. we are looking at it!"
This means that while Deepgram is aware of the potential usefulness of SSML and is considering its implementation, it is not currently available as a feature in their TTS API.
Instead of SSML, Deepgram offers alternative methods for controlling speech output, such as:
-
Using punctuation and ellipses for pausing, as described in the Text to Speech Prompting (https://developers.deepgram.com/docs/text-to-speech-prompting#pauses) documentation.
-
Utilizing natural language prompting techniques to achieve desired speech effects, as mentioned in the Text to Speech Prompting (https://developers.deepgram.com/docs/text-to-speech-prompting) guide.
If you need fine-grained control over speech synthesis similar to what SSML provides, you may need to use Deepgram's existing prompting techniques or wait for potential future updates that might include SSML support.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
pulsar jungle
@pearl forum Will the speech-to-speech understand me if I spell a word out vocally using the NATO alphabet?
pearl forumBOT
Let me check the knowledge base for you again. One moment! :books:
Based on the knowledge sources provided, there isn't specific information about Deepgram's speech-to-speech capability understanding words spelled out using the NATO alphabet. However, I can provide some relevant information about Deepgram's speech recognition capabilities that might be helpful.
Deepgram's speech recognition models are designed to transcribe spoken words, including spelled-out words. While there's no explicit mention of the NATO alphabet, the system does have some capabilities for handling spelled-out words:
-
According to the Pronunciation control documentation (https://developers.deepgram.com/docs/text-to-speech-prompting#pronunciation), Deepgram suggests using spelled-out words for pronunciation control in text-to-speech. This indicates that the system is capable of processing spelled-out words.
-
The same document provides a list of spelled-out alphabets that can be used for clear pronunciation: "Eigh, bee, sea, Dee, Eeeee, eff, gee, aitch, eye, Jay, Kay, Elle , Emm, En, Oh, pee, queue, Ar, ess, tee, you, vee, double you, eks, why, zee."
-
In a discussion about getting Deepgram to spell out words (https://github.com/orgs/deepgram/discussions/812), it's mentioned that Deepgram is working on improving alphabet pronunciation (A-Z).
However, it's important to note that Deepgram's models are trained to recognize natural speech patterns, and using a specialized alphabet like NATO might not yield perfect results. The system's ability to accurately transcribe spelled-out words using the NATO alphabet would depend on factors such as:
- The clarity of pronunciation
- The specific model being used
- The audio quality
If you need to ensure accurate recognition of spelled-out words, you might consider using Deepgram's keywords feature (https://developers.deepgram.com/docs/keywords) to boost the recognition of specific terms or alphabets.
For the most accurate information about Deepgram's current capabilities with the NATO alphabet, I recommend reaching out to Deepgram's support team or testing it with your specific use case.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
pulsar jungle
@pearl forum Tell you what. Give me a simple python example using my microphone for a speech-to-speech chatbot.
pearl forumBOT
Let me check the knowledge base for you again. One moment! :books:
valid umbraBOT
Hey there, it looks like you've been talking with Kapa for a bit. If you need additional help, you should consider moving to #1115960287183511643 where our team can answer your query.
pearl forumBOT
Certainly! I'd be happy to provide you with a simple Python example for a speech-to-speech chatbot using Deepgram's API and your microphone. This example will use Deepgram for both speech recognition and text-to-speech. Here's a basic implementation based on the knowledge sources provided:
import asyncio
import pyaudio
from deepgram import DeepgramClient, DeepgramClientOptions
# Replace with your Deepgram API key
DEEPGRAM_API_KEY = "YOUR_API_KEY"
async def main():
# Configure Deepgram client
config = DeepgramClientOptions(options={"keepalive": "true"})
deepgram = DeepgramClient(DEEPGRAM_API_KEY, config)
# Set up speech-to-text connection
stt_connection = deepgram.listen.live.v("1")
# Set up text-to-speech connection
tts_connection = deepgram.speak.websocket.v("1")
# Configure audio settings
CHUNK = 8000
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)
print("Listening... (Press Ctrl+C to stop)")
async def on_message(result):
transcript = result.channel.alternatives[0].transcript
if transcript:
print(f"You said: {transcript}")
# Simple chatbot logic (replace with your own)
response = f"I heard you say: {transcript}"
print(f"Bot response: {response}")
# Send response to text-to-speech
await tts_connection.send_text(response)
async def on_tts_audio(audio):
# Play the audio (you may need to implement audio playback)
print("Received TTS audio")
# Set up event handlers
stt_connection.on("Transcript", on_message)
tts_connection.on("AudioData", on_tts_audio)Start the connections
await stt_connection.start({"model": "nova-2", "language": "en-US"})
await tts_connection.start({"model": "aura-asteria-en"})
try:
while True:
data = stream.read(CHUNK)
await stt_connection.send(data)
except KeyboardInterrupt:
print("Stopping...")
finally:
stream.stop_stream()
stream.close()
p.terminate()
await stt_connection.finish()
await tts_connection.finish()
if name == "main":
asyncio.run(main())
This example provides a basic structure for a speech-to-speech chatbot using Deepgram. Here's what it does:
1. Sets up a Deepgram client for both speech-to-text (STT) and text-to-speech (TTS).
2. Configures a microphone input stream using PyAudio.
3. Listens for speech input and sends it to Deepgram's STT service.
4. When a transcript is received, it processes it with a simple chatbot logic (which you should replace with your own).
5. Sends the chatbot's response to Deepgram's TTS service.
6. Receives the TTS audio (note: you'll need to implement audio playback).
To use this example, you'll need to:
1. Install the required libraries: `pip install deepgram-sdk pyaudio`
2. Replace `YOUR_API_KEY` with your actual Deepgram API key.
3. Implement audio playback for the TTS response (the `on_tts_audio` function currently just prints a message).
This example is based on the Deepgram Python SDK examples for **microphone streaming** (<https://github.com/deepgram/deepgram-python-sdk/tree/main/examples/streaming/microphone>) and **text-to-speech** (<https://developers.deepgram.com/docs/python-sdk-streaming-text-to-speech>).Remember that this is a basic example and may need adjustments based on your specific requirements and environment. For production use, you'd want to add error handling, more sophisticated chatbot logic, and proper audio playback for the TTS responses.