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import gradio as gr
from transformers import pipeline
import numpy as np
transcriber = pipeline(
"automatic-speech-recognition",
model="openai/whisper-base.en",
return_timestamps=True,
)
def transcribe_live(state, words_list, new_chunk):
print(f"state: {state}")
try:
words_to_check_for = [word.strip() for word in words_list.split(",")]
except:
gr.Warning("Please enter a valid list of words to check for")
words_to_check_for = []
stream = state.get("stream", None)
previous_transcription = state.get("full_transcription", "")
previous_counts_of_words = state.get(
"counts_of_words", {word: 0 for word in words_to_check_for}
)
if new_chunk is None:
gr.Info("You can start transcribing by clicking on the Record button")
print("new chunk is None")
return state, previous_counts_of_words, previous_transcription
sr, y = new_chunk
# Convert to mono if stereo
if y.ndim > 1:
y = y.mean(axis=1)
y = y.astype(np.float32)
y /= np.max(np.abs(y))
if stream is not None:
stream = np.concatenate([stream, y])
else:
stream = y
try:
new_transcription = transcriber({"sampling_rate": sr, "raw": stream})
except Exception as e:
gr.Error(f"Transcription failed. Error: {e}")
print(f"Transcription failed. Error: {e}")
return state, previous_counts_of_words, previous_transcription
print(f"new transcription: {new_transcription}")
full_transcription_text = new_transcription["text"]
full_transcription_text_lower = full_transcription_text.lower()
new_counts_of_words = {
word: full_transcription_text_lower.count(word) for word in words_to_check_for
}
new_state = {
"stream": stream,
"full_transcription": full_transcription_text,
"counts_of_words": new_counts_of_words,
}
print(f"new state: {new_state}")
return new_state, new_counts_of_words, full_transcription_text
with gr.Blocks() as demo:
state = gr.State(
value={
"stream": None,
"full_transcription": "",
"counts_of_words": {},
}
)
filler_words = gr.Textbox(label="List of filer words", value="like, so, you know")
recording = gr.Audio(streaming=True, label="Recording")
word_counts = gr.JSON(label="Filler words count", value={})
transcription = gr.Textbox(label="Transcription", value="")
recording.stream(
transcribe_live,
inputs=[state, filler_words, recording],
outputs=[state, word_counts, transcription],
stream_every=5,
time_limit=60,
)
demo.launch(show_error=True)
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