Delete app.py

app.py

@@ -1,178 +0,0 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-import tensorflow_io as tfio
-from huggingface_hub import from_pretrained_keras
-
-
-model = from_pretrained_keras("keras-io/ctc_asr", compile=False)
-
-characters = [x for x in "abcdefghijklmnopqrstuvwxyz'?! "]
-# Mapping characters to integers
-char_to_num = keras.layers.StringLookup(vocabulary=characters, oov_token="")
-# Mapping integers back to original characters
-num_to_char = keras.layers.StringLookup(
-    vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True
-)
-
-# An integer scalar Tensor. The window length in samples.
-frame_length = 256
-# An integer scalar Tensor. The number of samples to step.
-frame_step = 160
-# An integer scalar Tensor. The size of the FFT to apply.
-# If not provided, uses the smallest power of 2 enclosing frame_length.
-fft_length = 384
-
-SAMPLE_RATE = 22050
-
-
-def decode_batch_predictions(pred):
-    input_len = np.ones(pred.shape[0]) * pred.shape[1]
-    # Use greedy search. For complex tasks, you can use beam search
-    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0]
-    # Iterate over the results and get back the text
-    output_text = []
-    for result in results:
-        result = tf.strings.reduce_join(num_to_char(result)).numpy().decode("utf-8")
-        output_text.append(result)
-    return output_text
-
-
-def load_16k_audio_wav(filename):
-    # Read file content
-    file_content = tf.io.read_file(filename)
-
-    # Decode audio wave
-    audio_wav, sample_rate = tf.audio.decode_wav(file_content, desired_channels=1)
-    audio_wav = tf.squeeze(audio_wav, axis=-1)
-    sample_rate = tf.cast(sample_rate, dtype=tf.int64)
-
-    # Resample to 16k
-    audio_wav = tfio.audio.resample(
-        audio_wav, rate_in=sample_rate, rate_out=SAMPLE_RATE
-    )
-
-    return audio_wav
-
-
-def mic_to_tensor(recorded_audio_file):
-    sample_rate, audio = recorded_audio_file
-
-    audio_wav = tf.constant(audio, dtype=tf.float32)
-    if tf.rank(audio_wav) > 1:
-        audio_wav = tf.reduce_mean(audio_wav, axis=1)
-    audio_wav = tfio.audio.resample(
-        audio_wav, rate_in=sample_rate, rate_out=SAMPLE_RATE
-    )
-
-    audio_wav = tf.divide(audio_wav, tf.reduce_max(tf.abs(audio_wav)))
-
-    return audio_wav
-
-
-def tensor_to_predictions(audio_tensor):
-    # 3. Change type to float
-    audio_tensor = tf.cast(audio_tensor, tf.float32)
-
-    # 4. Get the spectrogram
-    spectrogram = tf.signal.stft(
-        audio_tensor,
-        frame_length=frame_length,
-        frame_step=frame_step,
-        fft_length=fft_length,
-    )
-
-    # 5. We only need the magnitude, which can be derived by applying tf.abs
-    spectrogram = tf.abs(spectrogram)
-    spectrogram = tf.math.pow(spectrogram, 0.5)
-
-    # 6. normalisation
-    means = tf.math.reduce_mean(spectrogram, 1, keepdims=True)
-    stddevs = tf.math.reduce_std(spectrogram, 1, keepdims=True)
-    spectrogram = (spectrogram - means) / (stddevs + 1e-10)
-
-    spectrogram = tf.expand_dims(spectrogram, axis=0)
-
-    batch_predictions = model.predict(spectrogram)
-    batch_predictions = decode_batch_predictions(batch_predictions)
-    return batch_predictions
-
-
-def clear_inputs_and_outputs():
-    return [None, None, None]
-
-
-def predict(recorded_audio_file, uploaded_audio_file):
-    # 1. Read wav file
-    if recorded_audio_file:
-        audio_tensor = mic_to_tensor(recorded_audio_file)
-    else:
-        audio_tensor = load_16k_audio_wav(uploaded_audio_file)
-
-    prediction = tensor_to_predictions(audio_tensor)[0]
-    return prediction
-
-
-# gr.Interface(
-#     infer,
-#     inputs=gr.Audio(source="microphone", type="filepath"),
-#     outputs=gr.Textbox(lines=5, label="Input Text"),
-#     #title=title,
-#     #description=description,
-#     #article=article,
-#     #examples=examples,
-#     enable_queue=True,
-# ).launch(debug=True)
-
-# Main function
-if __name__ == "__main__":
-    demo = gr.Blocks()
-
-    with demo:
-        gr.Markdown(
-            """
-            <center><h1>Automatic Speech Recognition using CTC</h1></center> \
-            This space is a demo of Automatic Speech Recognition using Keras trained on LJSpeech dataset.<br> \
-    In this space, you can record your voice or upload a wav file and the model will predict the words spoken in English<br><br>
-            """
-        )
-        with gr.Row():
-            ## Input
-            with gr.Column():
-                mic_input = gr.Audio(source="microphone", label="Record your own voice")
-                upl_input = gr.Audio(
-                    source="upload", type="filepath", label="Upload a wav file"
-                )
-
-                with gr.Row():
-                    clr_btn = gr.Button(value="Clear", variant="secondary")
-                    prd_btn = gr.Button(value="Predict")
-
-            # Outputs
-            with gr.Column():
-                lbl_output = gr.Label(label="Text")
-
-        # Credits
-        with gr.Row():
-            gr.Markdown(
-                """
-                <h4>Credits</h4>
-                Author: <a href="https://twitter.com/anuragcomm"> Anurag Singh</a>.<br>
-                Based on the following Keras example <a href="https://keras.io/examples/audio/ctc_asr">Automatic Speech Recognition using CTC</a> by <a href="https://rbouadjenek.github.io/">Mohamed Reda Bouadjenek</a> and <a href="https://www.linkedin.com/in/parkerhuynh/">Ngoc Dung Huynh</a><br>
-                Check out the model <a href="https://huggingface.co/keras-io/ctc_asr">here</a>
-                """
-            )
-
-        clr_btn.click(
-            fn=clear_inputs_and_outputs,
-            inputs=[],
-            outputs=[mic_input, upl_input, lbl_output],
-        )
-        prd_btn.click(
-            fn=predict,
-            inputs=[mic_input, upl_input],
-            outputs=[lbl_output],
-        )
-
-    demo.launch(debug=True)