Update app.py
Browse files1. Support multi channels (mean stereo to mono)
2. Better recognition rate. (Whisper)
app.py
CHANGED
@@ -1,4 +1,3 @@
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from random import sample
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import gradio as gr
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import torchaudio
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@@ -10,8 +9,12 @@ import jiwer
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# ASR part
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from transformers import pipeline
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p = pipeline("automatic-speech-recognition")
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# WER part
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transformation = jiwer.Compose([
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jiwer.ToLowerCase(),
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@@ -44,7 +47,9 @@ class ChangeSampleRate(nn.Module):
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model = lightning_module.BaselineLightningModule.load_from_checkpoint("epoch=3-step=7459.ckpt").eval()
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def calc_mos(audio_path, ref):
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wav, sr = torchaudio.load(audio_path)
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osr = 16_000
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batch = wav.unsqueeze(0).repeat(10, 1, 1)
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csr = ChangeSampleRate(sr, osr)
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@@ -73,6 +78,7 @@ def calc_mos(audio_path, ref):
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return predic_mos, trans, wer, phone_transcription, ppm
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description ="""
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MOS prediction demo using UTMOS-strong w/o phoneme encoder model, which is trained on the main track dataset.
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This demo only accepts .wav format. Best at 16 kHz sampling rate.
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@@ -86,15 +92,13 @@ Add WER interface.
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iface = gr.Interface(
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fn=calc_mos,
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inputs=[gr.Audio(
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gr.Textbox(
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label="Reference")],
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outputs=[gr.Textbox(placeholder="Predicted MOS", label="Predicted MOS"),
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gr.Textbox(placeholder="Hypothesis", label="Hypothesis"),
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gr.Textbox(placeholder="Word Error Rate", label = "WER"),
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gr.Textbox(placeholder="Predicted Phonemes", label="Predicted Phonemes"),
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gr.Textbox(placeholder="Phonemes per minutes", label="PPM")],
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title="Laronix's Voice Quality Checking System Demo",
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description=description,
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allow_flagging="auto",
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from random import sample
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import gradio as gr
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import torchaudio
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# ASR part
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from transformers import pipeline
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# p = pipeline("automatic-speech-recognition")
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p = pipeline(
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"automatic-speech-recognition",
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model="KevinGeng/whipser_medium_en_PAL300_step25",
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device=0,
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)
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# WER part
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transformation = jiwer.Compose([
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jiwer.ToLowerCase(),
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model = lightning_module.BaselineLightningModule.load_from_checkpoint("epoch=3-step=7459.ckpt").eval()
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def calc_mos(audio_path, ref):
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wav, sr = torchaudio.load(audio_path, channels_first=True)
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if wav.shape[0] > 1:
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wav = wav.mean(dim=0, keepdim=True) # Mono channel
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osr = 16_000
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batch = wav.unsqueeze(0).repeat(10, 1, 1)
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csr = ChangeSampleRate(sr, osr)
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return predic_mos, trans, wer, phone_transcription, ppm
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description ="""
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MOS prediction demo using UTMOS-strong w/o phoneme encoder model, which is trained on the main track dataset.
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This demo only accepts .wav format. Best at 16 kHz sampling rate.
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iface = gr.Interface(
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fn=calc_mos,
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inputs=[gr.Audio(type='filepath', label="Audio to evaluate"),
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gr.Textbox(placeholder="Input reference here (Don't keep this empty)", label="Reference")],
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outputs=[gr.Textbox(placeholder="Naturalness evaluation, ranged 1 to 5, the higher the better.", label="Predicted MOS"),
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gr.Textbox(placeholder="Hypothesis", label="Hypothesis"),
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gr.Textbox(placeholder="Word Error Rate: Only valid when Reference is given", label = "WER"),
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gr.Textbox(placeholder="Predicted Phonemes", label="Predicted Phonemes"),
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gr.Textbox(placeholder="Speaking Rate, Phonemes per minutes", label="PPM")],
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title="Laronix's Voice Quality Checking System Demo",
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description=description,
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allow_flagging="auto",
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