Learn how to turn audio into text
Transcribe audio into whatever language the audio is in.
Use ttsmp3 to generate your audio data
save it as example.mp3 in your Downloads folder
Example text to use in ttsmp3 :
Welcome to the world of automatic speech recognition with OpenAI's Whisper API. Whisper is an automatic speech recognition system trained on a massive amount of multilingual and multitask supervised data collected from the web. It is designed to convert spoken language into written text, making transcription tasks a breeze.- Add a **break**: Mary had a little lamb <break time="1s"/> Whose fleece was white as snow.
- **Emphasizing**: I already told you I <emphasis level="strong">really like </emphasis> that person.
- **Speed**: For dramatic purposes, you might wish to <prosody rate="slow">slow down the speaking rate of your text.</prosody>
Or if you are in a hurry <prosody rate="fast">your may want to speed it up a bit.</prosody>
- **Pitch**: Do you like sythesized speech <prosody pitch="high">with a pitch that is higher than normal?</prosody> Or do you prefer your speech <prosody pitch="-20%">with a somewhat lower pitch?</prosody>
- **Whisper** <amazon:effect name="whispered">If you make any noise, </amazon:effect> she said, <amazon:effect name="whispered">they will hear us.</amazon:effect>
- **Conversations**: It is possible to switch between speakers within the text:
- [speaker:Brian] Hello Emma
- [speaker:Emma] Hey BrianFile uploads are currently limited to 25 MB and the following input file types are supported: mp3, mp4, mpeg, mpga, m4a, wav, and webm.
<OpenAIObject at 0x117dea450> JSON: {
"text": "Welcome to the world of Automatic Speech Recognition \
with OpenAI's Whisper API. Whisper is an automatic speech \
recognition system trained on a massive amount of multilingual \
and multitask supervised data collected from the web. It is designed \
to convert spoken language into written text, making transcription tasks a breeze."
}"Welcome to the world of Automatic Speech Recognition with OpenAI's Whisper API. Whisper is an automatic speech recognition system trained on a massive amount of multilingual and multitask supervised data collected from the web. It is designed to convert spoken language into written text, making transcription tasks a breeze."We will use an example with multiple .mp3 files which are stored in the ‘Downloads’ folder
Note that the files will be renamed during the process
data in your Downloads folderdata folderdef transcribe_audio(audio_file_path):
"""
Transcribe the given audio file using OpenAI's Whisper ASR system.
"""
audio_file_path = Path(audio_file_path) # Convert to Path object if it's not already
with audio_file_path.open("rb") as audio_file:
response = openai.Audio.transcribe("whisper-1", audio_file)
return response["text"]data_folder = destination_folder
# Get a list of all audio files in the /data/ folder
audio_files = sorted(data_folder.glob('*.mp3'))
for audio_file in audio_files:
# Transcribe the audio file
transcription = transcribe_audio(audio_file)
# Create a new txt file name based on the audio file's name
txt_file_name = audio_file.stem + ".txt"
txt_file_path = data_folder / txt_file_name
# Save the transcription to the txt file
with txt_file_path.open('w') as txt_file:
txt_file.write(transcription)markdown_content = []
# Iterate through all txt files in the /data/ folder
txt_files = sorted(data_folder.glob('*.txt'))
for txt_file in txt_files:
# Read the content of the txt file
with txt_file.open('r') as file:
file_content = file.read()
# Append the file name as a headline and then the content to the markdown content list
markdown_content.append(f"# {txt_file.name}\n\n{file_content}\n")
# Join all the markdown content pieces to create the final document
final_markdown = "\n".join(markdown_content)
# Save the combined content to a markdown file
output_file = data_folder / "combined_audio_transcriptions.md"
with output_file.open('w') as file:
file.write(final_markdown)def generate_corrected_transcript(temperature, system_prompt, transcribed_text):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": transcribed_text
}
]
)
return response['choices'][0]['message']['content']# Directory containing the transcription files
data_folder = destination_folder
# List to hold the corrected transcriptions
corrected_transcriptions = []
# Iterate through each individual .txt file
for txt_file_path in sorted(data_folder.glob('??_audio.txt')):
# Read the transcription from the file
with txt_file_path.open('r') as txt_file:
transcription = txt_file.read()
# Post-process the transcription using GPT-4
corrected_text = generate_corrected_transcript(0.5, system_prompt, transcription)
# Append the file name as a headline and then the corrected content to the list
corrected_transcriptions.append(f"# {txt_file_path.name}\n\n{corrected_text}\n")
# Combine the corrected transcriptions and save them to the new markdown file
output_file = data_folder / "corrected_text.md"
with output_file.open('w') as out_file:
out_file.write("\n".join(corrected_transcriptions))# Define file paths
original_file_path = Path.home() / 'Downloads/data/combined_audio_transcriptions.md'
corrected_file_path = Path.home() / 'Downloads/data/corrected_text.md'
# Read the content of both markdown files
with original_file_path.open('r') as f:
original_content = f.readlines()
with corrected_file_path.open('r') as f:
corrected_content = f.readlines()
# Generate a side-by-side comparison in HTML format
differ = difflib.HtmlDiff()
comparison_html = differ.make_file(original_content, corrected_content)
# Add custom CSS for content wrapping and better visualization
custom_css = """
<style>
body {
font-family: Arial, sans-serif;
margin: 20px;
}
table.diff {
width: 100%;
border-collapse: collapse;
}
td {
vertical-align: top;
padding: 5px;
border: 1px solid #ddd;
white-space: pre-wrap;
word-break: break-word;
overflow-wrap: break-word;
}
</style>
"""
# The rest of your code remains the same...
# Insert the custom CSS into the generated HTML content
comparison_html = comparison_html.replace('<head>', '<head>' + custom_css)
# Save the HTML comparison to a file
output_html_path = Path.home() / 'Downloads/data/comparison.html'
with output_html_path.open('w') as f:
f.write(comparison_html)Congratulations! You have completed this tutorial 👍
Next, you may want to go back to the lab’s website
# Read the transcriptions from the markdown file
markdown_path = Path('YOUR/PATH/DATA.md')
with markdown_path.open('r') as md_file:
transcriptions = md_file.read()
# Post-process the transcriptions using GPT-4
corrected_text = generate_corrected_transcript(0.2, system_prompt, transcriptions)
# save the corrected text to a new markdown file
output_path = Path('YOUR/PATH/corrected_data.md')
with output_path.open('w') as out_file:
out_file.write(corrected_text)Jan Kirenz