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6280a78 fc896e5 0e34747 ac965e5 43f0036 6280a78 fe5d88c 43f0036 fe5d88c 6280a78 fc896e5 6280a78 fc896e5 fe5d88c 43f0036 4bd759c 43f0036 fe5d88c 43f0036 fe5d88c 43f0036 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import streamlit as st
import whisper
import tempfile
import os
import torchaudio
from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration
import numpy as np
import soundfile as sf
from io import BytesIO
# Title and description
st.title("🎧 Whisper Audio Transcriber")
st.markdown("Upload a `.wav` or `.mp3` file or record audio using your microphone to get transcribed text with timestamps using Whisper.")
# Load Whisper model
@st.cache_resource
def load_model():
return whisper.load_model("base")
model = load_model()
st.success("✅ Whisper model loaded!")
# File uploader
audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3"])
# Microphone recording
st.subheader("🎙️ Record Audio")
RTC_CONFIGURATION = RTCConfiguration({"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]})
class AudioProcessor:
def __init__(self):
self.audio_buffer = []
def recv(self, frame):
self.audio_buffer.append(frame.to_ndarray())
return frame
ctx = webrtc_streamer(
key="audio-recorder",
mode=WebRtcMode.SENDONLY,
rtc_configuration=RTC_CONFIGURATION,
media_stream_constraints={"audio": True, "video": False},
audio_processor_factory=AudioProcessor,
)
if ctx.audio_processor:
if st.button("Stop and Transcribe Recording"):
if ctx.audio_processor.audio_buffer:
st.info("📝 Processing recorded audio...")
# Combine audio frames
audio_data = np.concatenate(ctx.audio_processor.audio_buffer, axis=0)
# Save as WAV file
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
sf.write(tmp_file.name, audio_data, 16000) # WebRTC typically uses 16kHz
temp_path = tmp_file.name
# Transcription
st.info("📝 Transcribing...")
result = model.transcribe(temp_path)
# Display segments
st.subheader("🕒 Segments with Timestamps")
for segment in result["segments"]:
st.markdown(f"**[{segment['start']:.2f}s - {segment['end']:.2f}s]**: {segment['text']}")
# Full transcription
st.subheader("🧾 Full Transcript")
st.text_area("Transcribed Text", result["text"], height=250, key="recorded_transcript")
# Clean up
os.remove(temp_path)
ctx.audio_processor.audio_buffer = [] # Clear buffer
else:
st.warning("⚠️ No audio recorded.")
# Process uploaded file
if audio_file is not None:
# Save uploaded file temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
tmp_file.write(audio_file.read())
temp_path = tmp_file.name
# Convert MP3 to WAV if needed
if audio_file.name.endswith(".mp3"):
waveform, sample_rate = torchaudio.load(temp_path)
wav_path = temp_path.replace(".wav", "_converted.wav")
torchaudio.save(wav_path, waveform, sample_rate)
os.remove(temp_path)
temp_path = wav_path
# Transcription
st.info("📝 Transcribing...")
result = model.transcribe(temp_path)
# Display segments
st.subheader("🕒 Segments with Timestamps")
for segment in result["segments"]:
st.markdown(f"**[{segment['start']:.2f}s - {segment['end']:.2f}s]**: {segment['text']}")
# Full transcription
st.subheader("🧾 Full Transcript")
st.text_area("Transcribed Text", result["text"], height=250, key="uploaded_transcript")
# Clean up
os.remove(temp_path) |