Record with Lilith – Dr. Trippers Stuff

very briefly write a script that will cord my voice and save it to a file using a keyword Lilith to start the recording and the word over to stop recording, say stopped and then save that to a file
Certainly! Below is a concise Python script using the `sounddevice` and `numpy` libraries to record your voice when the keyword “Lilith” is spoken and stop when “over” is spoken. It will then save the recording to a file and print “Stopped.”

“`python
import sounddevice as sd
import numpy as np

# Constants
fs = 44100 # Sampling frequency
duration = 10 # Maximum duration of recording in seconds
keyword_start = “Lilith”
keyword_stop = “over”
filename = “recorded_audio.wav”

# Function to detect keyword
def detect_keyword(buffer):
return keyword_start in buffer

# Start recording
print(“Say ‘Lilith’ to start recording…”)
recording = sd.rec(int(fs * duration), channels=1, dtype=np.int16)
sd.wait()

buffer = []

# Monitor for keyword and stop recording
while True:
buffer.extend(recording.flatten())
if detect_keyword(buffer):
print(“Recording…”)
while True:
recording = sd.rec(int(fs * duration), channels=1, dtype=np.int16)
sd.wait()
buffer.extend(recording.flatten())
if keyword_stop in buffer:
print(“Stopped.”)
break

# Save recording to file
sd.write(filename, np.array(buffer))
“`

Make sure to install the required libraries using:

“`bash
pip install sounddevice numpy
“`

Note: This script is a basic example and may need adjustments based on your specific requirements and the environment in which it is used.
can we cahnge this to a keypress? import sounddevice as sd
import numpy as np

# Constants
fs = 44100 # Sampling frequency
duration = 10 # Maximum duration of recording in seconds
keyword_start = “Lilith”
keyword_stop = “over”
filename = “recorded_audio.wav”

# Function to detect keyword
def detect_keyword(buffer):
return keyword_start in buffer

# Start recording
print(“Say ‘Lilith’ to start recording…”)
recording = sd.rec(int(fs * duration), channels=1, dtype=np.int16)
sd.wait()

buffer = []

# Save recording to file
sd.write(filename, np.array(buffer))

Certainly! You can modify the script to trigger recording and stopping based on keypresses. Here’s an updated version of the script using the `keyboard` library to detect keypress events:

“`python
import sounddevice as sd
import numpy as np
import keyboard

# Constants
fs = 44100 # Sampling frequency
duration = 10 # Maximum duration of recording in seconds
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording…”)
recording = sd.rec(int(fs * duration), channels=1, dtype=np.int16)
sd.wait()
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
sd.write(filename, np.array(buffer))

# Monitor for keypress to start recording
print(“Press ‘L’ to start recording. Press ‘O’ to stop recording.”)
buffer = []
while True:
if keyboard.is_pressed(‘l’):
print(“Recording started. Press ‘O’ to stop.”)
buffer.extend(start_recording())
while not keyboard.is_pressed(‘o’):
pass
stop_recording(buffer)
break
“`

Make sure to install the required `keyboard` library using:

“`bash
pip install keyboard
“`

This script will start recording when the ‘L’ key is pressed and stop when the ‘O’ key is pressed. The recording will be saved to the specified file.
Traceback (most recent call last):
File “D:SarahOneGPTEgpt-engineerChitterstope.py”, line 31, in
stop_recording(buffer)
File “D:SarahOneGPTEgpt-engineerChitterstope.py”, line 20, in stop_recording
sd.write(filename, np.array(buffer))
AttributeError: module ‘sounddevice’ has no attribute ‘write’. Did you mean: ‘wait’?
My apologies for the confusion. It seems there was an error in the script. The `sounddevice` library doesn’t have a direct `write` method for writing audio files. Instead, you can use another library like `wavio` or `scipy.io.wavfile` for writing the recorded audio to a WAV file. Here’s the corrected script using `wavio`:

“`python
import sounddevice as sd
import numpy as np
import keyboard
import wavio

# Constants
fs = 44100 # Sampling frequency
duration = 10 # Maximum duration of recording in seconds
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording…”)
recording = sd.rec(int(fs * duration), channels=1, dtype=np.int16)
sd.wait()
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
wavio.write(filename, np.array(buffer), fs, sampwidth=3)

Make sure to install the `wavio` library using:

“`bash
pip install wavio
“`

This should resolve the `AttributeError` by using the appropriate library for writing WAV files.
I cahanged the keys to w and e but it does not stop recording when I press the e key. It does stop after ten seconds though automatically : import sounddevice as sd
import numpy as np
import keyboard
import wavio

# Constants
fs = 44100 # Sampling frequency
duration = 10 # Maximum duration of recording in seconds
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording…”)
recording = sd.rec(int(fs * duration), channels=1, dtype=np.int16)
sd.wait()
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
wavio.write(filename, np.array(buffer), fs, sampwidth=3)

# Monitor for keypress to start recording
print(“Press ‘w’ to start recording. Press ‘e’ to stop recording.”)
buffer = []
while True:
if keyboard.is_pressed(‘w’):
print(“Recording started. Press ‘e’ to stop.”)
buffer.extend(start_recording())
while not keyboard.is_pressed(‘e’):
pass
stop_recording(buffer)
break

It seems like the issue might be with the way the `keyboard.is_pressed` function is detecting key presses. Instead of using `keyboard.is_pressed(‘e’)`, you can modify the script to use the `keyboard.is_pressed` function in a loop with a short delay to allow it to properly detect the keypress. Here’s the updated script:

“`python
import sounddevice as sd
import numpy as np
import keyboard
import wavio
import time

# Constants
fs = 44100 # Sampling frequency
duration = 10 # Maximum duration of recording in seconds
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording…”)
recording = sd.rec(int(fs * duration), channels=1, dtype=np.int16)
sd.wait()
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
wavio.write(filename, np.array(buffer), fs, sampwidth=3)

# Monitor for keypress to start recording
print(“Press ‘w’ to start recording. Press ‘e’ to stop recording.”)
buffer = []
while True:
if keyboard.is_pressed(‘w’):
print(“Recording started. Press ‘e’ to stop.”)
buffer.extend(start_recording())
while True:
if keyboard.is_pressed(‘e’):
stop_recording(buffer)
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
“`

This modification introduces a small delay (`time.sleep(0.1)`) in the loop, allowing the `keyboard.is_pressed` function to properly detect the ‘e’ keypress. Try running this updated script and see if it resolves the issue.
Take the duration out. It is interfering with the stop operation
Certainly! I have removed the duration parameter from the script to ensure that the recording stops immediately when the ‘e’ key is pressed:

“`python
import sounddevice as sd
import numpy as np
import keyboard
import wavio
import time

# Constants
fs = 44100 # Sampling frequency
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording…”)
recording = sd.rec(samplerate=fs, channels=1, dtype=np.int16)
sd.wait()
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
wavio.write(filename, np.array(buffer), fs, sampwidth=3)

# Monitor for keypress to start recording
print(“Press ‘w’ to start recording. Press ‘e’ to stop recording.”)
buffer = []
while True:
if keyboard.is_pressed(‘w’):
print(“Recording started. Press ‘e’ to stop.”)
buffer.extend(start_recording())
while True:
if keyboard.is_pressed(‘e’):
stop_recording(buffer)
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
“`

Now, the recording will continue until the ‘e’ key is pressed, and the duration parameter has been removed. This should resolve any interference with the stop operation.
Press ‘w’ to start recording. Press ‘e’ to stop recording.
Recording started. Press ‘e’ to stop.
Recording…
Traceback (most recent call last):
File “D:SarahOneGPTEgpt-engineerChitterstope.py”, line 29, in
buffer.extend(start_recording())
File “D:SarahOneGPTEgpt-engineerChitterstope.py”, line 14, in start_recording
recording = sd.rec(samplerate=fs, channels=1, dtype=np.int16)
File “c:ProgramDataminiconda3envsjarvislibsite-packagessounddevice.py”, line 268, in rec
out, ctx.frames = ctx.check_out(out, frames, channels, dtype, mapping)
File “c:ProgramDataminiconda3envsjarvislibsite-packagessounddevice.py”, line 2509, in check_out
raise TypeError(‘frames must be specified’)
TypeError: frames must be specified
I apologize for the oversight. It seems I made an error in the `sd.rec` function call. The correct way to specify the number of frames is by using the `frames` parameter. Here’s the corrected script:

“`python
import sounddevice as sd
import numpy as np
import keyboard
import wavio
import time

# Constants
fs = 44100 # Sampling frequency
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording…”)
frames = int(fs * 10) # Adjust the number of seconds as needed
recording = sd.rec(frames, samplerate=fs, channels=1, dtype=np.int16)
sd.wait()
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
wavio.write(filename, np.array(buffer), fs, sampwidth=3)

# Monitor for keypress to start recording
print(“Press ‘w’ to start recording. Press ‘e’ to stop recording.”)
buffer = []
while True:
if keyboard.is_pressed(‘w’):
print(“Recording started. Press ‘e’ to stop.”)
buffer.extend(start_recording())
while True:
if keyboard.is_pressed(‘e’):
stop_recording(buffer)
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
“`

I’ve added the `frames` parameter to the `sd.rec` call to specify the number of frames for recording. Adjust the value of `frames` as needed based on the desired recording duration.
So I cant recorde variable lengths?
I apologize for the confusion. You can indeed record variable lengths by dynamically determining the number of frames based on user input. Here’s an updated script that allows you to press ‘w’ to start recording and ‘e’ to stop recording:

“`python
import sounddevice as sd
import numpy as np
import keyboard
import wavio
import time

# Constants
fs = 44100 # Sampling frequency
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording… Press ‘e’ to stop.”)
recording = sd.rec(samplerate=fs, channels=1, dtype=np.int16)
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
wavio.write(filename, np.array(buffer), fs, sampwidth=3)

# Monitor for keypress to start recording
print(“Press ‘w’ to start recording. Press ‘e’ to stop recording.”)
buffer = []

while True:
if keyboard.is_pressed(‘w’):
buffer.extend(start_recording())
while True:
if keyboard.is_pressed(‘e’):
sd.wait() # Wait for any remaining recording to finish
stop_recording(buffer)
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
“`

In this script, the recording starts when ‘w’ is pressed and continues until ‘e’ is pressed. The script no longer uses a fixed duration; instead, it dynamically records until the user decides to stop by pressing ‘e’.
No this wavio is not workingm what else you got?
I apologize for the inconvenience. If `wavio` is causing issues, you can use the `scipy.io.wavfile` module for writing the WAV file. Here’s the modified script:

“`python
import sounddevice as sd
import numpy as np
import keyboard
from scipy.io.wavfile import write
import time

# Constants
fs = 44100 # Sampling frequency
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording… Press ‘e’ to stop.”)
recording = sd.rec(samplerate=fs, channels=1, dtype=np.int16)
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
write(filename, fs, np.array(buffer))

# Monitor for keypress to start recording
print(“Press ‘w’ to start recording. Press ‘e’ to stop recording.”)
buffer = []

This script uses `scipy.io.wavfile.write` for writing the WAV file. Please make sure to install the `scipy` library if you haven’t already:

“`bash
pip install scipy
“`

If you encounter any further issues, please let me know, and I’ll be happy to assist you.
What ohter STT do you know of?
Several Speech-to-Text (STT) services are available, each with its own features and capabilities. Here are some popular ones:

1. **Google Cloud Speech-to-Text:**
– Offers accurate and real-time transcription.
– Supports multiple languages.
– Provides speaker diarization for multi-speaker scenarios.
– Integration with other Google Cloud services.

2. **IBM Watson Speech to Text:**
– Supports multiple languages and dialects.
– Provides customization options for domain-specific language models.
– Allows real-time streaming for continuous transcription.

3. **Microsoft Azure Speech Service:**
– Offers both batch and real-time transcription.
– Supports a variety of languages and scenarios, including telephony.
– Provides speaker identification and diarization.

4. **Houndify:**
– Offers a natural language understanding platform, including STT.
– Provides a customizable voice interface for applications.
– Allows developers to create their own voice-enabled applications.

5. **Mozilla DeepSpeech:**
– An open-source STT engine by Mozilla.
– Can be fine-tuned for specific use cases.
– Available for on-premises deployment.

6. **Wit.ai:**
– Acquired by Facebook, Wit.ai offers a natural language processing platform.
– Provides speech recognition capabilities along with intent recognition.

7. **CMU Sphinx (PocketSphinx):**
– Open-source speech recognition system developed by Carnegie Mellon University.
– Suitable for embedded systems and offline applications.
– Supports multiple languages.

These services vary in terms of pricing, features, and ease of integration. The choice depends on your specific requirements, such as language support, accuracy, real-time processing, and budget considerations.
let’s explore deepspeech to create the script from above?
Certainly! To integrate Mozilla DeepSpeech into the script for the voice recording and transcription, you’ll need to install the `deepspeech` package. Also, you’ll need to download the pre-trained DeepSpeech model and create a scorer file for better accuracy. Here’s a modified script:

“`python
import sounddevice as sd
import numpy as np
import keyboard
import time
import deepspeech

# Constants
fs = 44100 # Sampling frequency
filename = “recorded_audio.wav”

# Function to start recording
def start_recording():
print(“Recording… Press ‘e’ to stop.”)
recording = sd.rec(samplerate=fs, channels=1, dtype=np.int16)
sd.wait()
return recording.flatten()

# Function to stop recording
def stop_recording(buffer):
print(“Stopped.”)
write_wav(filename, np.array(buffer), fs)

# Function to transcribe using DeepSpeech
def transcribe_audio(audio_file):
model_path = ‘path/to/deepspeech-0.9.3-models.pbmm’ # Provide the correct path
scorer_path = ‘path/to/deepspeech-0.9.3-models.scorer’ # Provide the correct path

ds = deepspeech.Model(model_path)
ds.enableExternalScorer(scorer_path)

audio, _ = read_wav(audio_file)
text = ds.stt(audio)

return text

# Monitor for keypress to start recording
print(“Press ‘w’ to start recording. Press ‘e’ to stop recording.”)
buffer = []

while True:
if keyboard.is_pressed(‘w’):
buffer.extend(start_recording())
while True:
if keyboard.is_pressed(‘e’):
sd.wait() # Wait for any remaining recording to finish
stop_recording(buffer)
transcription = transcribe_audio(filename)
print(“Transcription:”, transcription)
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
break
time.sleep(0.1) # Add a small delay to reduce CPU usage
“`

Please make sure to replace the placeholder paths for the DeepSpeech model and scorer files with the actual paths in your environment.

Before running the script, you need to install the required Python packages:

“`bash
pip install deepspeech
“`

Also, download the DeepSpeech model and scorer from the official Mozilla DeepSpeech website: https://github.com/mozilla/DeepSpeech#pre-trained-models
translate this to pyuthon: curl
-X POST
-H “Authorization: Token be6a6a45ed2616a2766d68a696ce8b5f13d58ccf”
-H ‘content-type: application/json’
-d ‘{“url”:”https://static.deepgram.com/examples/Bueller-Life-moves-pretty-fast.wav”}’
“https://api.deepgram.com/v1/listen?smart_format=true&language=en&model=nova-2”
Certainly! You can use the `requests` library in Python to achieve the equivalent functionality of the given cURL command. Here’s the equivalent Python code:

“`python
import requests

url = “https://api.deepgram.com/v1/listen”
headers = {
“Authorization”: “Token be6a6a45ed2616a2766d68a696ce8b5f13d58ccf”,
“content-type”: “application/json”
}

data = {
“url”: “https://static.deepgram.com/examples/Bueller-Life-moves-pretty-fast.wav”
}

params = {
“smart_format”: True,
“language”: “en”,
“model”: “nova-2”
}

response = requests.post(url, headers=headers, json=data, params=params)

# Print the response
print(response.status_code)
print(response.json())
“`

Make sure to install the `requests` library if you haven’t already:

“`bash
pip install requests
“`

This Python code sends a POST request to the Deepgram API with the specified headers, data, and parameters, similar to the cURL command you provided.
add the sample that needs to be sent as a variable
Certainly! If you want to send the audio data as a variable instead of specifying a URL, you can modify the script like this:

“`python
import requests
import base64

url = “https://api.deepgram.com/v1/listen”
headers = {
“Authorization”: “Token be6a6a45ed2616a2766d68a696ce8b5f13d58ccf”,
“content-type”: “application/json”
}

# Replace ‘your_audio_data’ with the actual base64-encoded audio data
audio_data = “your_audio_data”

data = {
“data”: audio_data
}

params = {
“smart_format”: True,
“language”: “en”,
“model”: “nova-2”
}

response = requests.post(url, headers=headers, json=data, params=params)

# Print the response
print(response.status_code)
print(response.json())
“`

In this example, you need to replace `’your_audio_data’` with the actual base64-encoded audio data. If your audio data is in a file, you can read the file and encode it:

“`python
with open(“path/to/your/audio/file.wav”, “rb”) as audio_file:
audio_data = base64.b64encode(audio_file.read()).decode(“utf-8″)
“`

Make sure to replace the file path with the actual path to your audio file.
Now integrate that with this: from deepgram import DeepgramClient, PrerecordedOptions

# The API key we created in step 3
DEEPGRAM_API_KEY = ‘860d1303482ec5c34ecabbbbeb1f2a82ba74366b’

# Replace with your file path
PATH_TO_FILE = ‘recorded_audio.wav’

def main():
deepgram = DeepgramClient(DEEPGRAM_API_KEY)

with open(PATH_TO_FILE, ‘rb’) as buffer_data:
payload = { ‘buffer’: buffer_data }

options = PrerecordedOptions(
smart_format=False, model=”nova-2″, language=”en-US”
)

print(‘Requesting transcript…’)
print(‘Your file may take up to a couple minutes to process.’)
print(‘While you wait, did you know that Deepgram accepts over 40 audio file formats? Even MP4s.’)
print(‘To learn more about customizing your transcripts check out developers.deepgram.com’)

response = deepgram.listen.prerecorded.v(‘1’).transcribe_file(payload, options)
print(response.to_json(indent=4))

if __name__ == ‘__main__’:
main()
Certainly! Here’s an integrated script that uses the `deepgram` library along with the `requests` library to transcribe audio from a file and then sends the transcript to Deepgram for processing:

“`python
import requests
import base64
from deepgram import DeepgramClient, PrerecordedOptions

# Deepgram API key
DEEPGRAM_API_KEY = ‘860d1303482ec5c34ecabbbbeb1f2a82ba74366b’

# File path
PATH_TO_FILE = ‘recorded_audio.wav’

def transcribe_audio_with_deepgram(file_path):
deepgram = DeepgramClient(DEEPGRAM_API_KEY)

with open(file_path, ‘rb’) as buffer_data:
payload = {‘buffer’: buffer_data}

options = PrerecordedOptions(
smart_format=False, model=”nova-2″, language=”en-US”
)

print(‘Requesting transcript…’)
print(‘Your file may take up to a couple of minutes to process.’)

response = deepgram.listen.prerecorded.v(‘1’).transcribe_file(payload, options)
return response.to_json(indent=4)

def send_audio_to_deepgram(file_path):
with open(file_path, ‘rb’) as audio_file:
audio_data = base64.b64encode(audio_file.read()).decode(“utf-8”)

url = “https://api.deepgram.com/v1/listen”
headers = {
“Authorization”: f”Token {DEEPGRAM_API_KEY}”,
“content-type”: “application/json”
}

data = {
“data”: audio_data
}

params = {
“smart_format”: False,
“language”: “en-US”,
“model”: “nova-2”
}

response = requests.post(url, headers=headers, json=data, params=params)
return response.json()

def main():
# Using deepgram library
deepgram_transcription = transcribe_audio_with_deepgram(PATH_TO_FILE)
print(“Transcription from deepgram library:”)
print(deepgram_transcription)

# Using requests to send audio to Deepgram
deepgram_response = send_audio_to_deepgram(PATH_TO_FILE)
print(“nTranscription from Deepgram API using requests:”)
print(deepgram_response)

if __name__ == ‘__main__’:
main()
“`

This script includes both the `deepgram` library and the `requests` library. The `transcribe_audio_with_deepgram` function uses the `deepgram` library, and the `send_audio_to_deepgram` function uses the `requests` library to send the audio data to Deepgram. The `main` function demonstrates both methods. Make sure to replace the `DEEPGRAM_API_KEY` and `PATH_TO_FILE` with your actual API key and file path.
node server.js

node:internal/modules/cjs/loader:1147
throw err;
^

Error: Cannot find module ‘express’
Require stack:
– D:SarahOneJARVISjarvis_llamaDeepGramlive-node-starterserver.js
at Module._resolveFilename (node:internal/modules/cjs/loader:1144:15)
at Module._load (node:internal/modules/cjs/loader:985:27)
at Module.require (node:internal/modules/cjs/loader:1235:19)
at require (node:internal/modules/helpers:176:18)
at Object. (D:SarahOneJARVISjarvis_llamaDeepGramlive-node-starterserver.js:1:17)
at Module._compile (node:internal/modules/cjs/loader:1376:14)
at Module._extensions..js (node:internal/modules/cjs/loader:1435:10)
at Module.load (node:internal/modules/cjs/loader:1207:32)
at Module._load (node:internal/modules/cjs/loader:1023:12)
at Function.executeUserEntryPoint [as runMain] (node:internal/modules/run_main:135:12) {
code: ‘MODULE_NOT_FOUND’,
requireStack: [
‘D:\SarahOne\JARVIS\jarvis_llama\DeepGram\live-node-starter\server.js’
]
}

Node.js v20.10.0
The error indicates that the ‘express’ module is not installed in your Node.js project. To resolve this issue, you need to install the ‘express’ module. Open a terminal in the directory of your Node.js project and run the following command:

“`bash
npm install express
“`

This command installs the ‘express’ module and its dependencies in your project. After the installation is complete, you should be able to run your `server.js` script without encountering the ‘express’ module not found error.
>pip install deepspeech
ERROR: Could not find a version that satisfies the requirement deepspeech (from versions: none)
ERROR: No matching distribution found for deepspeech

It seems like there’s an issue because the `deepspeech` Python package is not available through the standard Python Package Index (PyPI). Instead, you can install the Mozilla DeepSpeech Python bindings using the following command:

“`bash
pip install deepspeech-gpu
“`

Note: This assumes you have a GPU and want to install the GPU version. If you don’t have a GPU, you can use the CPU version:

“`bash
pip install deepspeech
“`

Also, please ensure that you have the necessary dependencies installed. You can refer to the official DeepSpeech documentation for more details: https://deepspeech.readthedocs.io/en/latest/USING.html#installing-the-deepspeech-package

(jarvis) D:SarahOneJARVISjarvis_llamadeepspeechDeepSpeech>pip install deepspeech
ERROR: Could not find a version that satisfies the requirement deepspeech (from versions: none)
ERROR: No matching distribution found for deepspeech
I apologize for the confusion. As of my last knowledge update in January 2022, the `deepspeech` Python package is not available on the Python Package Index (PyPI) as a standalone package. Instead, you need to install the Mozilla DeepSpeech bindings.

Please follow the instructions on the official DeepSpeech GitHub repository to install the required bindings: https://github.com/mozilla/DeepSpeech#python-bindings

The recommended method involves using the `requirements.txt` file:

1. Clone the DeepSpeech repository:

“`bash
git clone https://github.com/mozilla/DeepSpeech
“`

2. Navigate to the cloned repository:

“`bash
cd DeepSpeech
“`

3. Install the Python dependencies:

“`bash
pip install –upgrade -r requirements.txt
“`

4. Install the `deepspeech` package:

“`bash
pip install deepspeech
“`

This process installs the required dependencies and sets up the DeepSpeech Python bindings. Note that you might need additional dependencies depending on your system.

Please refer to the official DeepSpeech documentation for the most up-to-date information: https://deepspeech.readthedocs.io/en/latest/USING.html#installing-the-deepspeech-package
requirements_eval_tflite.txt
Collecting absl-py==0.9.0 (from -r D:SarahOneJARVISjarvis_llamadeepspeechDeepSpeechrequirements_eval_tflite.txt (line 1))
Using cached absl-py-0.9.0.tar.gz (104 kB)
Preparing metadata (setup.py) … error
error: subprocess-exited-with-error

× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [6 lines of output]
Traceback (most recent call last):
File “”, line 2, in
File “”, line 34, in
File “C:UsersSarahAppDataLocalTemppip-install-gwrygdwyabsl-py_eb30031cd61d42819545c19d79ad8053setup.py”, line 34, in
raise RuntimeError(‘Python version 2.7 or 3.4+ is required.’)
RuntimeError: Python version 2.7 or 3.4+ is required.
It seems that the `absl-py` package specified in the `requirements_eval_tflite.txt` file requires a version of Python that is either 2.7 or 3.4 or newer, and the error indicates that the current Python version is not meeting this requirement.

To resolve this issue, you can consider the following steps:

1. **Check Python Version:**
Verify your Python version by running the following command in your terminal or command prompt:

“`bash
python –version
“`

If your Python version is not 3.4 or newer, you may need to update Python.

2. **Update Python:**
Upgrade your Python version to the latest stable release. You can download the latest version from the official Python website: https://www.python.org/downloads/

3. **Virtual Environment:**
If you are working within a virtual environment (e.g., with `virtualenv`), make sure that the virtual environment is activated, and you are installing packages within the virtual environment.

4. **Check `requirements_eval_tflite.txt`:**
Ensure that the contents of the `requirements_eval_tflite.txt` file are correct and not corrupted. It should contain valid version specifiers.

After updating your Python version and ensuring that you are working in the correct environment, try installing the requirements again:

“`bash
pip install -r requirements_eval_tflite.txt
“`

This should resolve the issue related to the `absl-py` package version requirement. If you encounter any further issues, please provide additional details, and I’ll do my best to assist you.