Upload 10 files
Browse files- app.py +169 -0
- en_decoder.onnx +3 -0
- en_encoder.onnx +3 -0
- en_joiner.onnx +3 -0
- en_tokens.txt +652 -0
- fr_decoder.onnx +3 -0
- fr_encoder.onnx +3 -0
- fr_joiner.onnx +3 -0
- fr_tokens.txt +653 -0
- requirements.txt +5 -0
app.py
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import numpy as np
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import gradio as gr
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import torchaudio
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import torch
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from sherpa_onnx import OnlineRecognizer
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import time
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# Initialize the recognizer
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recognizer_en = OnlineRecognizer.from_transducer(
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tokens="en_tokens.txt",
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encoder="en_encoder.onnx",
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decoder="en_decoder.onnx",
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joiner="en_joiner.onnx",
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num_threads=1,
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decoding_method="modified_beam_search",
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debug=False
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)
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recognizer_fr = OnlineRecognizer.from_transducer(
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tokens="fr_tokens.txt",
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encoder="fr_encoder.onnx",
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decoder="fr_decoder.onnx",
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joiner="fr_joiner.onnx",
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num_threads=1,
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decoding_method="modified_beam_search",
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debug=False
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)
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def transcribe_audio_online_streaming(file, language):
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"""Generator for file transcription"""
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if file is None:
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yield "Please upload an audio file."
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return
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try:
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match language:
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case "English":
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recognizer = recognizer_en
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case "French":
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recognizer = recognizer_fr
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waveform, sample_rate = torchaudio.load(file.name)
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if sample_rate != 16000:
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resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
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waveform = resampler(waveform)
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sample_rate = 16000
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waveform_np = waveform.numpy()[0]
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total_samples = waveform_np.shape[0]
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s = recognizer.create_stream()
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chunk_size = 4000 # 0.25-second chunks
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offset = 0
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while offset < total_samples:
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end = offset + chunk_size
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chunk = waveform_np[offset:end]
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s.accept_waveform(sample_rate, chunk.tolist())
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while recognizer.is_ready(s):
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recognizer.decode_streams([s])
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yield recognizer.get_result(s)
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offset += chunk_size
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# Final processing
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tail_paddings = np.zeros(int(0.66 * sample_rate), dtype=np.float32)
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s.accept_waveform(sample_rate, tail_paddings.tolist())
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s.input_finished()
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while recognizer.is_ready(s):
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recognizer.decode_streams([s])
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yield recognizer.get_result(s)
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except Exception as e:
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yield f"Error: {e}"
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def transcribe_microphone_stream(audio_chunk, stream_state, language):
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"""Real-time microphone streaming transcription"""
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try:
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match language:
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case "English":
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recognizer = recognizer_en
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case "French":
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recognizer = recognizer_fr
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if audio_chunk is None: # End of stream
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if stream_state is not None:
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# Flush remaining audio
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tail_paddings = np.zeros(int(0.66 * 16000), dtype=np.float32)
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stream_state.accept_waveform(16000, tail_paddings.tolist())
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stream_state.input_finished()
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while recognizer.is_ready(stream_state):
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recognizer.decode_streams([stream_state])
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final_text = recognizer.get_result(stream_state)
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return final_text, None
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return "", None
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sample_rate, waveform_np = audio_chunk
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# Resample if needed
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if sample_rate != 16000:
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waveform = torch.from_numpy(waveform_np).float().unsqueeze(0)
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resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
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waveform = resampler(waveform)
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waveform_np = waveform.squeeze(0).numpy()
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sample_rate = 16000
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# Initialize stream if first chunk
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if stream_state is None:
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stream_state = recognizer.create_stream()
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# Process audio chunk
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stream_state.accept_waveform(sample_rate, waveform_np.tolist())
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# Decode available frames
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while recognizer.is_ready(stream_state):
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recognizer.decode_streams([stream_state])
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current_text = recognizer.get_result(stream_state)
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return current_text, stream_state
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except Exception as e:
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print(f"Stream error: {e}")
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return str(e), stream_state
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def create_app():
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with gr.Blocks() as app:
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gr.Markdown("# Real-time Speech Recognition")
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language_choice = gr.Radio(choices=["English", "French"], label="Select Language", value="English")
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with gr.Tabs():
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with gr.Tab("File Transcription"):
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gr.Markdown("Upload an audio file for streaming transcription")
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file_input = gr.File(label="Audio File", type="filepath")
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file_output = gr.Textbox(label="Transcription")
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transcribe_btn = gr.Button("Transcribe")
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transcribe_btn.click(lambda: "", outputs=file_output).then(
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transcribe_audio_online_streaming,
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inputs=[file_input, language_choice],
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outputs=file_output
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)
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with gr.Tab("Live Microphone"):
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gr.Markdown("Speak into your microphone for real-time transcription")
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mic = gr.Audio(
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sources=["microphone"],
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streaming=True,
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type="numpy",
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label="Live Input",
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show_download_button=False
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)
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live_output = gr.Textbox(label="Live Transcription")
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state = gr.State()
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mic.stream(
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transcribe_microphone_stream,
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inputs=[mic, state, language_choice],
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outputs=[live_output, state],
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show_progress="hidden"
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)
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return app
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if __name__ == "__main__":
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app = create_app()
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app.launch()
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en_decoder.onnx
ADDED
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version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:455ba38466fce8d5a57e7db68a323b684079ca4d9e1dd93a740d9b2429aae3b1
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size 617488
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en_encoder.onnx
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:d4881c57449d581e0770fd53fa66c2fdc6cd167d92ece7c715e603defc96d9d4
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size 70092599
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en_joiner.onnx
ADDED
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version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:d406f616736350e2a7df3e39398b78eb2fc1a2ca6973a19d3853fa3227e25b52
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3 |
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size 336817
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en_tokens.txt
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|
1 |
+
<blk> 0
|
2 |
+
<sos/eos> 1
|
3 |
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<unk> 2
|
4 |
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s 3
|
5 |
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▁the 4
|
6 |
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t 5
|
7 |
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, 6
|
8 |
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. 7
|
9 |
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▁a 8
|
10 |
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▁to 9
|
11 |
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' 10
|
12 |
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e 11
|
13 |
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▁and 12
|
14 |
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▁ 13
|
15 |
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ing 14
|
16 |
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▁of 15
|
17 |
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y 16
|
18 |
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n 17
|
19 |
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a 18
|
20 |
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d 19
|
21 |
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o 20
|
22 |
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▁you 21
|
23 |
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|
24 |
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▁I 23
|
25 |
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26 |
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i 25
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ed 26
|
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29 |
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re 28
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30 |
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m 29
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31 |
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r 30
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32 |
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p 31
|
33 |
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▁is 32
|
34 |
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al 33
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35 |
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▁we 34
|
36 |
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g 35
|
37 |
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er 36
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38 |
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▁s 37
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or 38
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40 |
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u 39
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c 40
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42 |
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ar 41
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f 42
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44 |
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in 43
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b 44
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46 |
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ll 47
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49 |
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50 |
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51 |
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|
52 |
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l 51
|
53 |
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54 |
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k 53
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55 |
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w 54
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56 |
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le 55
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57 |
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h 57
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59 |
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ve 58
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ly 61
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ri 62
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it 64
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66 |
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68 |
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69 |
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▁f 68
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70 |
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71 |
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|
72 |
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73 |
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|
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ent 74
|
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ur 75
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on 76
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ce 77
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79 |
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▁i 78
|
80 |
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81 |
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▁A 81
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an 82
|
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▁he 83
|
85 |
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|
86 |
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ch 85
|
87 |
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▁me 86
|
88 |
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ra 87
|
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|
90 |
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en 89
|
91 |
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ic 90
|
92 |
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▁not 91
|
93 |
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- 92
|
94 |
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|
95 |
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|
96 |
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97 |
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98 |
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th 97
|
99 |
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ter 98
|
100 |
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▁they 99
|
101 |
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▁what 100
|
102 |
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▁de 101
|
103 |
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ation 102
|
104 |
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ro 103
|
105 |
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ver 104
|
106 |
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ck 105
|
107 |
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▁your 106
|
108 |
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E 107
|
109 |
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te 108
|
110 |
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▁The 109
|
111 |
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▁all 110
|
112 |
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▁C 111
|
113 |
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▁know 112
|
114 |
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se 113
|
115 |
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il 114
|
116 |
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▁at 115
|
117 |
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▁d 116
|
118 |
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ad 117
|
119 |
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▁ca 118
|
120 |
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▁one 119
|
121 |
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A 120
|
122 |
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▁my 121
|
123 |
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▁So 122
|
124 |
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el 123
|
125 |
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▁there 124
|
126 |
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▁And 125
|
127 |
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▁if 126
|
128 |
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lo 127
|
129 |
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at 128
|
130 |
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▁g 129
|
131 |
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ate 130
|
132 |
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▁about 131
|
133 |
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▁go 132
|
134 |
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T 133
|
135 |
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? 134
|
136 |
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S 135
|
137 |
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▁up 136
|
138 |
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▁ma 137
|
139 |
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la 138
|
140 |
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▁out 139
|
141 |
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▁get 140
|
142 |
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▁an 141
|
143 |
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▁from 142
|
144 |
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▁se 143
|
145 |
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▁T 144
|
146 |
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ir 145
|
147 |
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O 146
|
148 |
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x 147
|
149 |
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I 148
|
150 |
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▁see 149
|
151 |
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▁co 150
|
152 |
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ment 151
|
153 |
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▁mo 152
|
154 |
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us 153
|
155 |
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as 154
|
156 |
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et 155
|
157 |
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▁going 156
|
158 |
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ng 157
|
159 |
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▁some 158
|
160 |
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he 159
|
161 |
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▁M 160
|
162 |
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hi 161
|
163 |
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▁here 162
|
164 |
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li 163
|
165 |
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▁con 164
|
166 |
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▁B 165
|
167 |
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▁ho 166
|
168 |
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▁pa 167
|
169 |
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▁P 168
|
170 |
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me 169
|
171 |
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v 170
|
172 |
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▁bo 171
|
173 |
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ci 172
|
174 |
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est 173
|
175 |
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▁W 174
|
176 |
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▁D 175
|
177 |
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ne 176
|
178 |
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un 177
|
179 |
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▁will 178
|
180 |
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▁want 179
|
181 |
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▁don 180
|
182 |
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▁really 181
|
183 |
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▁O 182
|
184 |
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▁L 183
|
185 |
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ect 184
|
186 |
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▁think 185
|
187 |
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▁because 186
|
188 |
+
▁H 187
|
189 |
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z 188
|
190 |
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om 189
|
191 |
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▁us 190
|
192 |
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ho 191
|
193 |
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▁more 192
|
194 |
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ol 193
|
195 |
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▁time 194
|
196 |
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▁our 195
|
197 |
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▁when 196
|
198 |
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▁li 197
|
199 |
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ge 198
|
200 |
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▁no 199
|
201 |
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ity 200
|
202 |
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▁F 201
|
203 |
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age 202
|
204 |
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▁mi 203
|
205 |
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qu 204
|
206 |
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▁right 205
|
207 |
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▁su 206
|
208 |
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▁by 207
|
209 |
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N 208
|
210 |
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▁po 209
|
211 |
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ul 210
|
212 |
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▁how 211
|
213 |
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ut 212
|
214 |
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▁which 213
|
215 |
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▁now 214
|
216 |
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▁fa 215
|
217 |
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0 216
|
218 |
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▁1 217
|
219 |
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▁look 218
|
220 |
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ow 219
|
221 |
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▁then 220
|
222 |
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act 221
|
223 |
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▁would 222
|
224 |
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▁who 223
|
225 |
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ies 224
|
226 |
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vi 225
|
227 |
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ry 226
|
228 |
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▁- 227
|
229 |
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▁N 228
|
230 |
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L 229
|
231 |
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ting 230
|
232 |
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▁people 231
|
233 |
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ive 232
|
234 |
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am 233
|
235 |
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ers 234
|
236 |
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ight 235
|
237 |
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▁R 236
|
238 |
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▁ex 237
|
239 |
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▁G 238
|
240 |
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im 239
|
241 |
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nd 240
|
242 |
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▁them 241
|
243 |
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ide 242
|
244 |
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: 243
|
245 |
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▁le 244
|
246 |
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ta 245
|
247 |
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▁very 246
|
248 |
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▁pro 247
|
249 |
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ke 248
|
250 |
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ma 249
|
251 |
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▁these 250
|
252 |
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▁E 251
|
253 |
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▁work 252
|
254 |
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ally 253
|
255 |
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is 254
|
256 |
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▁fi 255
|
257 |
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▁also 256
|
258 |
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able 257
|
259 |
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▁We 258
|
260 |
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▁his 259
|
261 |
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▁la 260
|
262 |
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ru 261
|
263 |
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▁make 262
|
264 |
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▁has 263
|
265 |
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▁o 264
|
266 |
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ig 265
|
267 |
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R 266
|
268 |
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id 267
|
269 |
+
▁say 268
|
270 |
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ist 269
|
271 |
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lu 270
|
272 |
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ld 271
|
273 |
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▁lo 272
|
274 |
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▁had 273
|
275 |
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▁other 274
|
276 |
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ion 275
|
277 |
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▁into 276
|
278 |
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ther 277
|
279 |
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▁their 278
|
280 |
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▁way 279
|
281 |
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▁ha 280
|
282 |
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tion 281
|
283 |
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▁J 282
|
284 |
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▁You 283
|
285 |
+
▁sp 284
|
286 |
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D 285
|
287 |
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▁back 286
|
288 |
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▁were 287
|
289 |
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▁need 288
|
290 |
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▁It 289
|
291 |
+
▁ba 290
|
292 |
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▁un 291
|
293 |
+
▁over 292
|
294 |
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mp 293
|
295 |
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ous 294
|
296 |
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pe 295
|
297 |
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▁use 296
|
298 |
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▁k 297
|
299 |
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C 298
|
300 |
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▁where 299
|
301 |
+
▁did 300
|
302 |
+
Y 301
|
303 |
+
▁ne 302
|
304 |
+
and 303
|
305 |
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ach 304
|
306 |
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ound 305
|
307 |
+
▁got 306
|
308 |
+
H 307
|
309 |
+
▁any 308
|
310 |
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sion 309
|
311 |
+
ine 310
|
312 |
+
▁even 311
|
313 |
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! 312
|
314 |
+
▁ra 313
|
315 |
+
▁little 314
|
316 |
+
mo 315
|
317 |
+
▁take 316
|
318 |
+
▁ro 317
|
319 |
+
5 318
|
320 |
+
▁pre 319
|
321 |
+
▁than 320
|
322 |
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one 321
|
323 |
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▁been 322
|
324 |
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j 323
|
325 |
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co 324
|
326 |
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▁let 325
|
327 |
+
ction 326
|
328 |
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▁comp 327
|
329 |
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ance 328
|
330 |
+
▁dis 329
|
331 |
+
▁well 330
|
332 |
+
▁op 331
|
333 |
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▁good 332
|
334 |
+
de 333
|
335 |
+
▁lot 334
|
336 |
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um 335
|
337 |
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U 336
|
338 |
+
▁she 337
|
339 |
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ant 338
|
340 |
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▁two 339
|
341 |
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▁But 340
|
342 |
+
▁kind 341
|
343 |
+
▁could 342
|
344 |
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▁first 343
|
345 |
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der 344
|
346 |
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pp 345
|
347 |
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▁start 346
|
348 |
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▁down 347
|
349 |
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▁actually 348
|
350 |
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▁fe 349
|
351 |
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ub 350
|
352 |
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▁per 351
|
353 |
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▁those 352
|
354 |
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ture 353
|
355 |
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M 354
|
356 |
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▁mean 355
|
357 |
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▁come 356
|
358 |
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▁things 357
|
359 |
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end 358
|
360 |
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op 359
|
361 |
+
▁her 360
|
362 |
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▁year 361
|
363 |
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ving 362
|
364 |
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les 363
|
365 |
+
P 364
|
366 |
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per 365
|
367 |
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ha 366
|
368 |
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mb 367
|
369 |
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ish 368
|
370 |
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ten 369
|
371 |
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▁off 370
|
372 |
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▁much 371
|
373 |
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▁He 372
|
374 |
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▁app 373
|
375 |
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▁something 374
|
376 |
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▁vi 375
|
377 |
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ence 376
|
378 |
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ud 377
|
379 |
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ard 378
|
380 |
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tic 379
|
381 |
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▁sa 380
|
382 |
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ical 381
|
383 |
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▁tra 382
|
384 |
+
▁U 383
|
385 |
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▁through 384
|
386 |
+
▁part 385
|
387 |
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ure 386
|
388 |
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▁inter 387
|
389 |
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ff 388
|
390 |
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▁gonna 389
|
391 |
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▁thing 390
|
392 |
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▁comm 391
|
393 |
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▁This 392
|
394 |
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1 393
|
395 |
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vo 394
|
396 |
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▁again 395
|
397 |
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4 396
|
398 |
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ated 397
|
399 |
+
▁di 398
|
400 |
+
▁new 399
|
401 |
+
▁different 400
|
402 |
+
▁him 401
|
403 |
+
ize 402
|
404 |
+
▁talk 403
|
405 |
+
9 404
|
406 |
+
ition 405
|
407 |
+
▁exp 406
|
408 |
+
▁sc 407
|
409 |
+
▁bit 408
|
410 |
+
2 409
|
411 |
+
▁K 410
|
412 |
+
▁mu 411
|
413 |
+
▁only 412
|
414 |
+
▁hu 413
|
415 |
+
ful 414
|
416 |
+
lic 415
|
417 |
+
ca 416
|
418 |
+
▁ta 417
|
419 |
+
▁th 418
|
420 |
+
ugh 419
|
421 |
+
▁day 420
|
422 |
+
▁cha 421
|
423 |
+
▁give 422
|
424 |
+
▁put 423
|
425 |
+
▁show 424
|
426 |
+
▁man 425
|
427 |
+
▁every 426
|
428 |
+
cu 427
|
429 |
+
▁In 428
|
430 |
+
▁most 429
|
431 |
+
▁video 430
|
432 |
+
row 431
|
433 |
+
▁help 432
|
434 |
+
no 433
|
435 |
+
▁car 434
|
436 |
+
▁try 435
|
437 |
+
▁feel 436
|
438 |
+
nk 437
|
439 |
+
▁said 438
|
440 |
+
▁sha 439
|
441 |
+
▁\ 440
|
442 |
+
▁should 441
|
443 |
+
00 442
|
444 |
+
▁en 443
|
445 |
+
▁yeah 444
|
446 |
+
▁That 445
|
447 |
+
F 446
|
448 |
+
com 447
|
449 |
+
▁uh 448
|
450 |
+
▁gu 449
|
451 |
+
▁pri 450
|
452 |
+
ven 451
|
453 |
+
▁great 452
|
454 |
+
... 453
|
455 |
+
▁Ma 454
|
456 |
+
▁around 455
|
457 |
+
▁add 456
|
458 |
+
6 457
|
459 |
+
▁many 458
|
460 |
+
8 459
|
461 |
+
▁um 460
|
462 |
+
▁same 461
|
463 |
+
▁find 462
|
464 |
+
ian 463
|
465 |
+
▁though 464
|
466 |
+
▁jo 465
|
467 |
+
port 466
|
468 |
+
▁before 467
|
469 |
+
▁end 468
|
470 |
+
iv 469
|
471 |
+
▁2 470
|
472 |
+
▁still 471
|
473 |
+
▁after 472
|
474 |
+
▁play 473
|
475 |
+
line 474
|
476 |
+
V 475
|
477 |
+
▁point 476
|
478 |
+
▁high 477
|
479 |
+
▁long 478
|
480 |
+
▁okay 479
|
481 |
+
▁love 480
|
482 |
+
ible 481
|
483 |
+
▁life 482
|
484 |
+
▁happen 483
|
485 |
+
▁three 484
|
486 |
+
▁V 485
|
487 |
+
▁place 486
|
488 |
+
▁THE 487
|
489 |
+
▁big 488
|
490 |
+
▁next 489
|
491 |
+
▁might 490
|
492 |
+
7 491
|
493 |
+
▁why 492
|
494 |
+
▁sure 493
|
495 |
+
▁20 494
|
496 |
+
▁va 495
|
497 |
+
\ 496
|
498 |
+
▁question 497
|
499 |
+
▁Now 498
|
500 |
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ment 91
|
93 |
+
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|
94 |
+
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|
95 |
+
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|
96 |
+
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|
97 |
+
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|
98 |
+
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|
99 |
+
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|
100 |
+
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|
101 |
+
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|
102 |
+
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|
103 |
+
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|
104 |
+
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|
105 |
+
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|
106 |
+
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|
107 |
+
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|
108 |
+
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|
109 |
+
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|
110 |
+
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|
111 |
+
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|
112 |
+
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|
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|
114 |
+
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|
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|
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|
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|
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|
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|
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+
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|
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+
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|
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|
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127 |
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|
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+
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+
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|
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|
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|
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|
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636 |
+
! 635
|
637 |
+
: 636
|
638 |
+
ą 637
|
639 |
+
? 638
|
640 |
+
Ò 639
|
641 |
+
Ú 640
|
642 |
+
Ć 641
|
643 |
+
ċ 642
|
644 |
+
Ğ 643
|
645 |
+
ĥ 644
|
646 |
+
ħ 645
|
647 |
+
ĩ 646
|
648 |
+
Ī 647
|
649 |
+
ĭ 648
|
650 |
+
Ń 649
|
651 |
+
#0 650
|
652 |
+
#1 651
|
653 |
+
#2 652
|
requirements.txt
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gradio>=5.0
|
2 |
+
torch
|
3 |
+
torchaudio
|
4 |
+
sherpa-onnx
|
5 |
+
numpy
|