Create app.py

app.py

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+import streamlit as st
+import torch
+import tempfile
+import os
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from audiorecorder import audiorecorder
+from pydub import AudioSegment
+
+# Setup model
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+model_id = "KBLab/kb-whisper-tiny"
+
+@st.cache_resource
+def load_model():
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_id, torch_dtype=torch_dtype, use_safetensors=True, cache_dir="cache"
+    )
+    model.to(device)
+    processor = AutoProcessor.from_pretrained(model_id)
+    return pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        torch_dtype=torch_dtype,
+        device=device,
+    )
+
+pipe = load_model()
+
+def transcribe_audio(audio_path):
+    return pipe(audio_path, chunk_length_s=30, generate_kwargs={"task": "transcribe", "language": "sv"})
+
+st.title("Speech-to-Text Transcription")
+
+# Audio recording
+st.subheader("Record Audio")
+recorded_audio = audiorecorder("Start Recording", "Stop Recording")
+
+if recorded_audio:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
+        temp_file.write(recorded_audio.tobytes())
+        temp_file_path = temp_file.name
+    st.audio(temp_file_path, format="audio/wav")
+    result = transcribe_audio(temp_file_path)
+    st.write("### Transcription:")
+    st.write(result["text"])
+    os.remove(temp_file_path)
+
+# File upload
+st.subheader("Upload Audio File")
+uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "ogg", "flac"])
+
+if uploaded_file:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[-1]) as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_file_path = temp_file.name
+    st.audio(temp_file_path)
+    result = transcribe_audio(temp_file_path)
+    st.write("### Transcription:")
+    st.write(result["text"])
+    os.remove(temp_file_path)