2nd

app.py

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+import torch
+import soundfile as sf
+from transformers import AutoModelForCTC, Wav2Vec2BertProcessor
+from pydub import AudioSegment
+import streamlit as st
+import tempfile
+
+# Define available models
+available_models = ['Yehor/w2v-bert-2.0-uk']
+
+st.title("Voice Recognition App")
+
+# Model selection dropdown
+model_name = st.selectbox("Choose a model", available_models)
+
+# # Config
+# device = 'cpu' # 'cuda:0' # or cpu
+# sampling_rate = 16_000
+
+# Load the model
+asr_model = AutoModelForCTC.from_pretrained(model_name).to(device)
+processor = Wav2Vec2BertProcessor.from_pretrained(model_name)
+
+# paths = [
+#   'short_1_16k.wav',
+# ]
+
+def map_to_pred(file_path, sampling_rate = 16_000, device = 'cpu'):
+    audio_inputs = []
+
+    # # load audio file
+    # audio, _ = librosa.load(file_path)
+    #
+    # # preprocess audio and generate standard
+    # input_features = processor([audio], return_tensors="pt", sampling_rate=16000).input_features
+    # generated_ids = model.generate(inputs=input_features)
+    # transcription = processor.batch_decode(generated_ids, normalize=True, skip_special_tokens=True)
+    # text = processor.tokenizer._normalize(transcription[0])
+
+    audio_input, _ = sf.read(file_path)
+    audio_inputs.append(audio_input)
+    # Transcribe the audio
+    inputs = processor(audio_inputs, sampling_rate=sampling_rate).input_features
+    features = torch.tensor(inputs).to(device)
+
+    with torch.no_grad():
+        logits = asr_model(features).logits
+
+    predicted_ids = torch.argmax(logits, dim=-1)
+    predictions = processor.batch_decode(predicted_ids)
+
+    # Log results
+    print('Predictions:')
+
+    return predictions
+
+
+# Extract audio
+# audio_inputs = []
+# for path in paths:
+#   audio_input, _ = sf.read(path)
+#   audio_inputs.append(audio_input)
+
+# # Transcribe the audio
+# inputs = processor(audio_inputs, sampling_rate=sampling_rate).input_features
+# features = torch.tensor(inputs).to(device)
+
+uploaded_file = st.file_uploader("Choose file", type=["wav", "mp3"])
+
+if uploaded_file is not None:
+    # convert file object to file path
+    file_path = './temp.wav'
+    with open(file_path, 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Save the uploaded file temporarily
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_file_path = temp_file.name
+
+    # Convert audio file to a format supported by Whisper (if necessary)
+    audio = AudioSegment.from_file(temp_file_path)
+    temp_wav_path = tempfile.mktemp(suffix=".wav")
+    audio.export(temp_wav_path, format="wav")
+
+    st.audio(uploaded_file, format="audio/wav")
+
+    text = map_to_pred(file_path)
+
+    # display results
+    st.write('Input audio:', uploaded_file.name)
+    st.write('Predicted standard:', text)

requirements.txt

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+streamlit
+transformers
+torch
+soundfile
+pydub