Using gTTS and Whisper. In this project we are going to use Google TTS to capture user spoken input. This is gTTS: gTTS (Google Text-to-Speech), a Python library and CLI tool to interface with Google Translate’s text-to-speech API. Writes spoken mp3 data to a file, a file-like object (bytestring) for further audio manipulation, or stdout. It features flexible pre-processing and tokenizing. I may not need Whisper is this does both.
Certainly! It seems like you’re planning to use gTTS (Google Text-to-Speech) for capturing user spoken input and generating corresponding audio output. gTTS is a Python library and CLI tool that interacts with Google Translate’s text-to-speech API. It can write spoken mp3 data to a file, a file-like object for further audio manipulation, or directly to stdout. The library provides flexibility in pre-processing and tokenizing text.
As for Whisper, it’s important to note that Whisper is not explicitly mentioned in your description, but if you are referring to Mozilla’s Whisper ASR (Automatic Speech Recognition) system, it’s a separate tool focused on speech recognition rather than text-to-speech synthesis. If your project involves both capturing user spoken input and generating audio responses, gTTS alone should be sufficient for the text-to-speech part.
In summary, gTTS can be a comprehensive solution for your project’s text-to-speech needs, while Whisper or another ASR system might be considered for speech recognition if that functionality is required in your project.
It seems there might be a bit of confusion regarding the tools you mentioned. Let’s clarify:
1. **gTTS (Google Text-to-Speech)**: This is indeed a Python library and command-line interface tool that allows you to convert text to speech using Google’s Text-to-Speech API. It generates spoken MP3 data from the text you provide.
2. **Whisper**: It’s not clear what you’re referring to here. There isn’t a widely-known tool or library called “Whisper” related to text-to-speech or audio manipulation. If you meant something specific, please provide more details so I can assist you accurately.
If you’re aiming to capture user spoken input and then use gTTS to convert it to text, you might be looking for a speech recognition library like SpeechRecognition. gTTS is typically used to convert text to speech, not the other way around.
If you clarify your requirements or provide more context, I can offer more specific guidance.
I don’t see where gtts is caputring audio and converting it to text
I apologize for any confusion. You are correct; my previous response was not accurate in reflecting gTTS’s capabilities. gTTS (Google Text-to-Speech) is primarily used for converting text to speech, not for capturing audio and converting it to text. If you need to capture user spoken input and convert it to text, you would need a Speech-to-Text (STT) system.
For this purpose, you might want to consider using a dedicated Speech-to-Text library or API, such as Google Cloud Speech-to-Text, Microsoft Azure Speech SDK, or Mozilla’s DeepSpeech. These tools are designed to transcribe spoken words into written text.
In summary, for capturing audio and converting it to text, you will need to integrate a Speech-to-Text solution alongside or instead of gTTS.
Which are local, fastest and python
For a local, fast, and Python-friendly Speech-to-Text solution, you might consider using Mozilla’s DeepSpeech. Here’s a brief overview:
**DeepSpeech by Mozilla:**
– **Local Processing:** DeepSpeech is an open-source Automatic Speech Recognition (ASR) engine developed by Mozilla. You can run it locally on your machine, allowing for processing without relying on an external server.
– **Speed:** DeepSpeech is optimized for speed and can provide real-time speech recognition on modern hardware.
– **Python Integration:** DeepSpeech provides Python bindings, making it easy to integrate with Python-based applications.
To use DeepSpeech, you would need to install the DeepSpeech library and download the pre-trained models. You can find more information, including installation instructions and documentation, on the official DeepSpeech GitHub repository: [Mozilla DeepSpeech](https://github.com/mozilla/DeepSpeech)
Keep in mind that the effectiveness of the Speech-to-Text solution can also depend on the specific use case, language, and other factors. It’s recommended to review the documentation and test the system to ensure it meets your project requirements.
I can get you the script: KeyError: ‘chat’
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File “D:SarahOneJARVISeva.py”, line 61, in
eva_response = send_to_eva(messages)
File “D:SarahOneJARVISeva.py”, line 33, in send_to_eva
completion = eva_client.chat.completions.create(
File “c:ProgramDataminiconda3envsjarvislibsite-packagesopenaiopenai_object.py”, line 61, in __getattr__
raise AttributeError(*err.args)
AttributeError: chat
The error you’re encountering (`KeyError: ‘chat’`) is likely due to an issue with accessing the ‘chat’ attribute in your OpenAI GPT-3 API request. It’s important to ensure that you’re using the correct attribute names and handling the OpenAI API response appropriately.
Here are a few things to check and troubleshoot:
1. **Check OpenAI API Version:**
Ensure that you are using the correct version of the OpenAI API and that your code is compatible with that version. The error might occur if you’re using an outdated or incompatible version.
2. **Verify API Request:**
Double-check your API request to ensure that you are using the correct endpoint (`openai.ChatCompletion.create`) and providing the necessary parameters. The ‘chat’ attribute should be part of your request payload.
Example:
“`python
completion = openai.ChatCompletion.create(
model=”gpt-3.5-turbo”,
messages=[
{“role”: “system”, “content”: “You are a helpful assistant.”},
{“role”: “user”, “content”: “Who won the world series in 2020?”},
]
)
“`
Make sure the ‘messages’ attribute is present in your request, and the roles (‘system’ and ‘user’) are specified correctly.
3. **Check API Response:**
Ensure that you are handling the API response correctly. The ‘chat’ attribute should be present in the response, and you should access it accordingly.
Example:
“`python
eva_response = completion[‘choices’][0][‘message’][‘content’]
“`
The actual attribute names may vary depending on the structure of the API response, so check the response structure to access the ‘chat’ content correctly.
If the issue persists, please provide the relevant portions of your code (excluding sensitive information) for a more detailed analysis.
import speech_recognition as sr
import pyttsx3
from openai import ChatCompletion
# Initialize Eva connection
eva_client = ChatCompletion(base_url=”http://localhost:5151/v1/chat/completions”, api_key=”not-needed”)
eva_history = [
{“role”: “system”, “content”: “You are an intelligent assistant. You always provide well-reasoned answers that are both correct and helpful.”},
{“role”: “user”, “content”: “Hello, introduce yourself to someone opening this program for the first time. Be concise.”},
]
# Initialize speech recognition
r = sr.Recognizer()
def record_text():
while True:
try:
with sr.Microphone() as source:
r.adjust_for_ambient_noise(source, duration=0.2)
print(“Listening…”)
audio = r.listen(source, timeout=10)
text = r.recognize_google(audio)
print(“You said:”, text)
return text
except sr.UnknownValueError:
print(“Sorry, I did not understand. Please repeat.”)
except sr.RequestError as e:
print(f”Could not request results from Google Speech Recognition service; {e}”)
def send_to_eva(messages):
completion = eva_client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=messages,
temperature=0.7,
stream=True,
)
new_message = {“role”: “assistant”, “content”: “”}
for chunk in completion:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end=””, flush=True)
new_message[“content”] += chunk.choices[0].delta.content
messages.append(new_message)
return new_message[“content”]
def speak_text(command):
engine = pyttsx3.init()
engine.say(command)
engine.runAndWait()
# Main loop
messages = eva_history.copy()
while True:
user_input = record_text()
messages.append({“role”: “user”, “content”: user_input})
eva_response = send_to_eva(messages)
speak_text(eva_response)
Thank you for providing the script. It seems like the error is related to how you’re trying to access the `chat` attribute in the OpenAI API response. The ‘chat’ attribute is not directly present in the response structure, and that’s causing the `KeyError: ‘chat’`. Instead, you should extract the assistant’s response from the chunks of the completion.
Here’s an updated version of your `send_to_eva` function:
“`python
def send_to_eva(messages):
completion = eva_client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=messages,
temperature=0.7,
stream=True,
)
new_message = {“role”: “assistant”, “content”: “”}
for chunk in completion:
if chunk[‘choices’][0][‘message’][‘content’]:
print(chunk[‘choices’][0][‘message’][‘content’], end=””, flush=True)
new_message[“content”] += chunk[‘choices’][0][‘message’][‘content’]
messages.append(new_message)
return new_message[“content”]
“`
In this version, I’ve adjusted how the content is accessed within the loop over the completion chunks. Please try making this change and see if it resolves the `KeyError` issue.
Listening…
You said: try to hear anything
Traceback (most recent call last):
File “c:ProgramDataminiconda3envsjarvislibsite-packagesopenaiopenai_object.py”, line 59, in __getattr__
return self[k]
KeyError: ‘chat’
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File “D:SarahOneJARVISeva.py”, line 62, in
eva_response = send_to_eva(messages)
File “D:SarahOneJARVISeva.py”, line 33, in send_to_eva
completion = eva_client.chat.completions.create(
File “c:ProgramDataminiconda3envsjarvislibsite-packagesopenaiopenai_object.py”, line 61, in __getattr__
raise AttributeError(*err.args)
AttributeError: chat
I apologize for the confusion. It appears there’s an issue with how you’re trying to access the ‘chat’ attribute in the OpenAI API response. The ‘chat’ attribute is not directly present in the response structure. Let’s modify the code accordingly.
Please update the `send_to_eva` function as follows:
“`python
def send_to_eva(messages):
completion = eva_client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=messages,
temperature=0.7,
stream=True,
)
new_message = {“role”: “assistant”, “content”: “”}
for chunk in completion[‘choices’]:
if ‘message’ in chunk and ‘content’ in chunk[‘message’]:
print(chunk[‘message’][‘content’], end=””, flush=True)
new_message[“content”] += chunk[‘message’][‘content’]
messages.append(new_message)
return new_message[“content”]
“`
In this updated version, I’ve adjusted how the content is accessed within the loop over the completion chunks. We check if ‘message’ and ‘content’ are present in each chunk before attempting to access them. This should resolve the `KeyError` issue.
Please try making this change and see if it resolves the problem.
You keep leaving the chat in
I apologize for the oversight. It seems there’s an issue with handling the completion chunks. Let’s make sure we’re correctly handling the streaming completion.
Update the `send_to_eva` function as follows:
“`python
def send_to_eva(messages):
completion = eva_client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=messages,
temperature=0.7,
stream=True,
)
new_message = {“role”: “assistant”, “content”: “”}
for chunk in completion:
if ‘choices’ in chunk and chunk[‘choices’][0][‘message’][‘content’]:
print(chunk[‘choices’][0][‘message’][‘content’], end=””, flush=True)
new_message[“content”] += chunk[‘choices’][0][‘message’][‘content’]
messages.append(new_message)
return new_message[“content”]
“`
This modification ensures that we iterate through the chunks correctly, considering the structure of the streaming response from the OpenAI API.
Please try this updated code, and it should address the issue of leaving the chat in.
RIGHT HERE!!!!!—->>> def send_to_eva(messages):
completion = eva_client.chat.completions.create(
I apologize for the confusion. It seems that my previous responses did not fully address the issue. I appreciate your patience.
Let’s make sure we handle the streaming completion correctly. Update the `send_to_eva` function as follows:
“`python
def send_to_eva(messages):
completion = eva_client.chat.create(
model=”local-model”,
messages=messages,
temperature=0.7,
)
new_message = {“role”: “assistant”, “content”: “”}
for choice in completion[‘choices’]:
if ‘message’ in choice and ‘content’ in choice[‘message’]:
print(choice[‘message’][‘content’], end=””, flush=True)
new_message[“content”] += choice[‘message’][‘content’]
messages.append(new_message)
return new_message[“content”]
“`
In this updated version, I replaced `eva_client.chat.completions.create` with `eva_client.chat.create` to match the latest OpenAI API. Additionally, I modified the loop to iterate over the ‘choices’ directly.
Please try this updated code, and it should address the issue of leaving the chat in.
I get it. chat.completions.create in openai 0.28.0 and I need openai > 1.3 for the connection string to work with a local model.
I appreciate your clarification. It seems you are using OpenAI version 0.28.0, and the `chat.completions.create` method might not be available in that version. If you need to use a local model with a connection string, you may indeed need to upgrade to a more recent version of the OpenAI Python library.
To upgrade OpenAI to the latest version, you can use the following command:
“`bash
pip install –upgrade openai
“`
Make sure to check the OpenAI documentation for any breaking changes or new features introduced in the version you are upgrading to. After the upgrade, you should be able to use the appropriate method for creating completions with your local model.
If you encounter any issues during the upgrade or have further questions, feel free to ask for assistance.
You are not very attentive
I apologize for any confusion and oversight. If there’s anything specific you’d like assistance with or if there are additional details you’d like to provide, please let me know. I’m here to help.