Fix invalid wav file error when converting from microphone to file (#1987)

* ;q
:wq

* Add demo

* Add unit test

* Fix test

* Slight refactor

* Lint

* Delete some recordings - import module not method

demo/unispeech-speaker-verification/requirements.txt

@@ -0,0 +1,2 @@
+git+https://github.com/huggingface/transformers
+torchaudio

demo/unispeech-speaker-verification/run.py

@@ -0,0 +1,120 @@
+import gradio as gr
+import torch
+from torchaudio.sox_effects import apply_effects_file
+from transformers import AutoFeatureExtractor, AutoModelForAudioXVector
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+STYLE = """
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" integrity="sha256-YvdLHPgkqJ8DVUxjjnGVlMMJtNimJ6dYkowFFvp4kKs=" crossorigin="anonymous">
+"""
+OUTPUT_OK = (
+    STYLE
+    + """
+    <div class="container">
+        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
+        <div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div>
+        <div class="row"><h1 style="text-align: center">similar</h1></div>
+        <div class="row"><h1 class="text-success" style="text-align: center">Welcome, human!</h1></div>
+        <div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
+    </div>
+"""
+)
+OUTPUT_FAIL = (
+    STYLE
+    + """
+    <div class="container">
+        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
+        <div class="row"><h1 class="display-1 text-danger" style="text-align: center">{:.1f}%</h1></div>
+        <div class="row"><h1 style="text-align: center">similar</h1></div>
+        <div class="row"><h1 class="text-danger" style="text-align: center">You shall not pass!</h1></div>
+        <div class="row"><small style="text-align: center">(You must get at least 85% to be considered the same person)</small><div class="row">
+    </div>
+"""
+)
+
+EFFECTS = [
+    ["remix", "-"],
+    ["channels", "1"],
+    ["rate", "16000"],
+    ["gain", "-1.0"],
+    ["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
+    ["trim", "0", "10"],
+]
+
+THRESHOLD = 0.85
+
+model_name = "microsoft/unispeech-sat-base-plus-sv"
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)
+cosine_sim = torch.nn.CosineSimilarity(dim=-1)
+
+
+def similarity_fn(path1, path2):
+    if not (path1 and path2):
+        return '<b style="color:red">ERROR: Please record audio for *both* speakers!</b>'
+    wav1, _ = apply_effects_file(path1, EFFECTS)
+    wav2, _ = apply_effects_file(path2, EFFECTS)
+    print(wav1.shape, wav2.shape)
+
+    input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
+    input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
+
+    with torch.no_grad():
+        emb1 = model(input1).embeddings
+        emb2 = model(input2).embeddings
+    emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()
+    emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()
+    similarity = cosine_sim(emb1, emb2).numpy()[0]
+
+    if similarity >= THRESHOLD:
+        output = OUTPUT_OK.format(similarity * 100)
+    else:
+        output = OUTPUT_FAIL.format(similarity * 100)
+
+    return output
+
+
+inputs = [
+    gr.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"),
+    gr.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"),
+]
+output = gr.HTML(label="")
+
+
+description = (
+    "This demo will compare two speech samples and determine if they are from the same speaker. "
+    "Try it with your own voice!"
+)
+article = (
+    "<p style='text-align: center'>"
+    "<a href='https://huggingface.co/microsoft/unispeech-sat-large-sv' target='_blank'>🎙️ Learn more about UniSpeech-SAT</a> | "
+    "<a href='https://arxiv.org/abs/2110.05752' target='_blank'>📚 UniSpeech-SAT paper</a> | "
+    "<a href='https://www.danielpovey.com/files/2018_icassp_xvectors.pdf' target='_blank'>📚 X-Vector paper</a>"
+    "</p>"
+)
+examples = [
+    ["samples/cate_blanch.mp3", "samples/cate_blanch_2.mp3"],
+    ["samples/cate_blanch.mp3", "samples/cate_blanch_3.mp3"],
+    ["samples/cate_blanch_2.mp3", "samples/cate_blanch_3.mp3"],
+    ["samples/heath_ledger.mp3", "samples/heath_ledger_2.mp3"],
+    ["samples/cate_blanch.mp3", "samples/kirsten_dunst.wav"],
+]
+
+demo = gr.Interface(
+    fn=similarity_fn,
+    inputs=inputs,
+    outputs=output,
+    title="Voice Authentication with UniSpeech-SAT + X-Vectors",
+    description=description,
+    article=article,
+    layout="horizontal",
+    theme="huggingface",
+    allow_flagging="never",
+    live=False,
+    examples=examples,
+)
+
+if __name__ == "__main__":
+    demo.launch()
+

gradio/components.py

@@ -2079,20 +2079,20 @@ class Audio(Changeable, Clearable, Playable, Streamable, IOComponent):
             file_obj = processing_utils.decode_base64_to_file(
                 file_data, file_path=file_name
             )
-        if crop_min != 0 or crop_max != 100:
-            sample_rate, data = processing_utils.audio_from_file(
-                file_obj.name, crop_min=crop_min, crop_max=crop_max
-            )
+        sample_rate, data = processing_utils.audio_from_file(
+            file_obj.name, crop_min=crop_min, crop_max=crop_max
+        )
+        if self.type == "numpy":
+            return sample_rate, data
+        elif self.type in ["file", "filepath"]:
             processing_utils.audio_to_file(sample_rate, data, file_obj.name)
-        if self.type == "file":
-            warnings.warn(
-                "The 'file' type has been deprecated. Set parameter 'type' to 'filepath' instead.",
-            )
-            return file_obj
-        elif self.type == "filepath":
-            return file_obj.name
-        elif self.type == "numpy":
-            return processing_utils.audio_from_file(file_obj.name)
+            if self.type == "file":
+                warnings.warn(
+                    "The 'file' type has been deprecated. Set parameter 'type' to 'filepath' instead.",
+                )
+                return file_obj
+            else:
+                return file_obj.name
         else:
             raise ValueError(
                 "Unknown type: "

test/test_components.py

@@ -12,6 +12,7 @@ import numpy as np
 import pandas as pd
 import PIL
 import pytest
+from scipy.io import wavfile
 
 import gradio as gr
 from gradio import media_data
@@ -1902,5 +1903,12 @@ def test_dataframe_postprocess_only_dates():
     }
 
 
+def test_audio_preprocess_can_be_read_by_scipy():
+    x_wav = deepcopy(media_data.BASE64_MICROPHONE)
+    audio_input = gr.Audio(type="filepath")
+    output = audio_input.preprocess(x_wav)
+    wavfile.read(output)
+
+
 if __name__ == "__main__":
     unittest.main()